Third-party Agent Research Articles

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products

In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

In edge computing, a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion. To maximize such benefits, this paper proposes a dynamic multi-attribute resource bidding mechanism (DMRBM). Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits. It is worth noting that when edge providers and users trade with third-party agents which are not entirely reliable and trustworthy, their sensitive information is prone to be leaked. Moreover, the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process, which is also very challenging. Therefore, this paper first adopts a privacy protection algorithm to prevent sensitive information from leakage. On the premise that the sensitive data of both edge providers and users are protected, the prices of providers fluctuate within a certain range. Then, users can choose appropriate edge providers by the price-performance ratio (PPR) standard and the reward of lower price (LPR) standard according to their demands. The two standards can be evolved by two evaluation functions. Furthermore, this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial time. Specifically, this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game theory. Through the extensive experiments, this paper demonstrates that the DMRBM satisfies the individual rationality, budget balance, and privacy protection and it can also increase the task offloading rate and the system benefits.

Adoption, Memory, and Cold War Greece: Kid pro quo? by Gonda Van Steen

Reviewed by: Adoption, Memory, and Cold War Greece: Kid pro quo? by Gonda Van Steen Jack Neubauer Adoption, Memory, and Cold War Greece: Kid pro quo? By Gonda Van Steen. Ann Arbor: University of Michigan Press, 2019. xx + 330 pp. Cloth $85.00, paper $39.95. Since the conclusion of World War II, hundreds of thousands of Asian children have been adopted by families in the United States and across the Global North. This story is well known. Successive waves of international adoptions from Korea, Vietnam, China, and elsewhere were celebrated in the media and popular culture at the time, and more recently they have been the subject of a growing body of critical scholarship by Catherine Ceniza Choy, Arissa Oh, Leslie Wang, and others. In contrast, the story of the approximately 3,200 Greek children adopted by American families during the Cold War remains largely unknown. As a result, readers of Gonda Van Steen's meticulously researched book, Adoption, Memory, and Cold War Greece, may be surprised to learn that during the "early Cold War period" (1948–1962), adoptions of Greek children to the United States far outpaced even those of Korean children on a per capita basis. An accomplished scholar of ancient Greek theater, Van Steen was inspired to research the history of Cold War Greek adoptions when she received an email from a young man named Mike, whose mother and aunt were adopted by a Greek American family after their father, Greek Communist Elias Argyriadis, [End Page 158] was executed by the Greek state in 1952. The intergenerational story of the Argyriadis family provides a narrative anchor for Van Steen's analysis of Greek-to-American adoptions, which she argues were shaped by the interconnected histories of the Greek Civil War (1946–1949), the global Cold War, and the broader international adoption movement. Adoption, Memory, and Cold War Greece is divided into three parts. Part I analyzes the combustible mixture of "push factors" driving the initial wave of Greek-to-American adoptions in the early 1950s. These include the dire social conditions of post–Civil War Greece (as of 1950, an estimated one in eight Greek children had lost one or both parents), violent persecution of left-wing families, and the Greek state's efforts, led by Queen Frederica, to cultivate American sympathy for "orphan Greece" while alleviating its social welfare burdens. In Part II, Van Steen demonstrates how early efforts to "find families" for Greek orphans transformed into a movement that was driven primarily by "the pull factor of the American demand for available white children" (125). The American Hellenic Educational Progressive Association (AHEPA), the largest Greek American voluntary association, helped meet this demand for Greek children through "proxy" adoptions in which a third-party agent matched prospective parents and Greek children with little screening or oversight. The result, Van Steen argues, was "a full-blown black market for babies" that enriched greedy Greek and American middlemen, who resorted to bribery and coercion to reclassify the stigmatized children of Greek single mothers as "adoptable orphans" (124). Based on extensive interviews, Part III presents a montage of individual adoption stories that illuminate the diversity of adoptees' attitudes and experiences. First, Van Steen introduces us to Dionysios Dionou, who refers to himself as a "stolen child" and calls his adopters "purchasers" rather than parents (185–87). A few pages later, we meet Dean, whose adoptive family lovingly joke that the $500 in fees they paid to adopt him was "the deal of a lifetime." Dean even framed the check his American parents used to pay for his adoption as symbol of their special family history (189–90). If the many individual stories in this section eventually start to bleed together in the reader's mind, it only underscores Van Steen's argument that they are constitutive of an emerging "collective subjectivity among Greek adoptees" (239). Adoption, Memory, and Cold War Greece is intended for both academic specialists and Greek adoptees interested in understanding the complex history behind their own personal journeys. For readers interested in the broader histories of childhood and adoption, Van Steen's in-depth analysis of Cold War Greece provides a fascinating case study for...

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

The prevalence of e-commerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third-party agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating [Formula: see text]-differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. This paper was accepted by J. George Shanthikumar, big data analytics.

Impartial pricing approach in double auction transactive distribution systems

This paper introduces a new trading mechanism for future transactive distribution systems with multiple private microgrids. An independent economic entity as a third-party agent is designed to facilitate the secure information sharing and providing fair market among participants in the transactive system. Proposed trading mechanism is based on double auction market clearing which shares benefits of each transaction between corresponding seller and buyer. To ensure the network’s technical constraints, real time operation of system is distributed between the players according to alternating direction method of multipliers (ADMM). The presented model satisfies complicated constraints and determines optimal scheduling. The proposed methodology is evaluated by implementing real-time operation on a modified 123- bus test distribution system with five micro-grids. Numerical studies show the beneficial properties of the proposed method.

When do incomplete contracts matter?

This paper derives conditions under which the introduction of a third-party agent solves the renegotiation-proofness problem of Moore and Repullo (1988)-type mechanisms, without introducing the potential for other agents to collude with the third-party. The key novelties of our mechanism are: (i) the introduction of a third-party agent only off-equilibrium and with some probability; (ii) the fact that both its existence and its identity are unknown to the other agents. We show that under these conditions, which are satisfied in many empirical applications, a hidden third-party agent can restore the implementation of the efficient allocation. If this agent does not observe the state of the world, we provide a sufficient condition for implementation to succeed.

The impacts of money-back guarantees in the presence of parallel importation

This paper studies the impacts of money-back guarantees (MBGs) in the presence of parallel importation, where a manufacturer sells a product facing customer fit uncertainties in two markets and a parallel importer diverts unauthorized products from the low-price market to the high-price one. The two firms compete in both pricing and MBG strategies. We consider two types of parallel importers, a third-party agent and an authorized retailer in the low-price market, respectively. By applying the certainty equivalent pricing approach, we show that offering MBGs only changes firms’ marginal costs without affecting customers’ purchasing decisions directly. When the net salvage values of returned products are positive in the high-price market, both the manufacturer and the third-party agent offer MBGs. Whenever the manufacturer offers an MBG in the low-price market, there is a price inflation effect that counters parallel importation. Therefore the manufacturer may offer the MBG even if the net salvage value is negative. When the authorized retailer decides refund policies in two markets, the MBG strategy in one market changes the retailer’s profit from the authorized channel and that from the gray market in opposite directions, and has an interaction with the refund policy in another market. No refund may be preferred in either market even if the net salvage value is positive. We find that the manufacturer’s MBG strategy in the high-price market always deters parallel importation while other MBG strategies may achieve Pareto improvements in the equilibrium.

Brief Study to Explore Trust and Security Challenges in Cloud Computing

Cloud Computing is an online strategy for dynamic allocation of services by reducing the usage of hardware and software resources. Existing models of cloud computing include different components: end-user computers, contact networks, access control frameworks, and software infrastructures. The data and the cloud infrastructure must be protected from any known and unknown attacks on all cloud platforms to achieve comprehensive cloud security. Trust issues arise from uncertainty about certain actions that can be defined through the concept of fuzzy belief. Trust is faith in quality provided by the cloud service provider and third-party agent on consumers and vice-versa where quality can be considered in terms of functionality, security, privacy, data safety, and reliability on cloud resources. Although the members of a social network have a strong level of trust, this trust may not be adequate in certain cases and becomes an obstacle in the successful implementation of cloud services. For example, a storage facility where users are vulnerable to missing, infected, or corrupted data, while providers are at risk of unknown data being hosted from their network. The relationship between two business partners decays more rapidly relative to the trust of users in the social network of the cloud. This paper aims to address the trust issues in existing cloud paradigms and highlights the existing flaws which will be resolved in the future course of action.

Designing a Transactive Framework for Future Distribution Systems

This article introduces a novel transactive energy management mechanism for future distribution systems with private microgrids. A third-party agent based on a transactive energy trading mechanism is designed which provides secure information sharing between participants. The proposed third-part agent acts as an independent economic entity (IEE) and clears the local energy market between the microgrid and distribution system. IEE determines the transactions corresponding to the maximum social welfare based on participants' submitted economic values. The economic values of entities are bids for buying and offers for selling the power. Two different pricing strategies for the approved transactions are presented. Distributed optimization for the self-scheduling based on an alternating direction method of multipliers has been implemented. Distributed optimization in a rolling fashion satisfies equality of complicated voltage magnitude, and voltage angles constraints at the connection buses. Furthermore, the participation of the distribution system in the day-ahead and real-time markets besides the transactive market is studied. The proposed structure is evaluated on a test distribution system with three microgrids in terms of energy trading analysis, and operation scheduling. COMP: Please remove colon appearing after table number in all the table captions.

A Blockchain-based Model for Cloud Service Quality Monitoring

This paper introduces a novel blockchain-based decentralized federation model that embodies quality verification for cloud providers who lease computing resources from each other. The blockchain structure removes the barriers of a traditional centralized federation and offers a fully distributed and transparent administration by enforcing the involved agents to maintain consensus on the data. For a blockchain-based federation, it is vital to avoid blind-trust on the claimed SLA guarantees and monitor the quality of service which is highly desirable considering the multi-tenancy characteristic of cloud services. Due to the fact that the blockchain network is unable to access the outside world, it cannot handle, by its own, providers misbehavior in terms of SLA violations. Thus, we introduce oracle as a verifier agent to monitor the quality of the service and report to the smart contract agents deployed on the blockchain. Oracle is a trusted third-party agent who can communicate with the outside world of the blockchain network. The interaction between cloud service providers (either providing a service or requesting it from another provider) and the oracle through smart contracts comprises a system of autonomous and utility maximizer agents. Cloud requesters seek to receive high quality services with constant monitoring at cheap prices or even with no charge, while cloud providers aim to have a balanced work-load with less preserved capacity, and the oracle tends to charge higher for their monitoring services. Therefore, to model this conflicting situation, we formulate a dynamic Stackelberg differential game to optimize the cost of using the oracle and maximize the profit of the agents with the role of provider agent as a leader, and the requester and verifier agents as followers. Our designed Stackelberg differential game can seize the dynamicity of users’ demand and resource provisioning in a competitive cloud market. We implemented our proposed decentralized model using the Solidity language in the remix IDE on the Ethereum network. We further evaluated the optimal controls and agents’ profit with real-world data simulated for three concrete cloud providers. The results revealed that the requester agent initiates most of the quality verification requests at the beginning to the middle time of the contract. Thus, the provider agent could reserve less computing resources considering the fact that it could share the workload among other customers’ computing resources during the peak-time. Moreover, imposing a higher penalty on the provider agent increased the capacity and decreased the number of requests for quality verification at the equilibrium. The evaluation also disclosed that the impact of timing in the dynamic pricing strategy of the verifier agent is very minimal, and the provisioning capacity of the provider is strongly correlated with the monitoring price.

Probabilistic reward or punishment promotes cooperation in evolutionary games

Reward and punishment are crucial for the emergence and sustainability of cooperation in evolutionary games. In this work, we introduce a third-party agent, who plays the role of a judge to reward cooperators and punish defectors, to study the impact of reward and punishment on the evolution of cooperation. The introduced righteous agent is different from the cooperators and defectors in the traditional games and it exists as a judge independent of the processes of the games. In each round of the evolutionary game, each player has a half chance to confront the righteous agent. If the player is a cooperator, it’s possible for it to obtain an extra profit. On the contrary, when a defector meets the righteous agent, its earnings may be reduced. The simulation results show that the introduction of the righteous agent in the evolutionary game favors the evolution of cooperation. The robustness of the promoting effect is tested for different complex topologies for the prisoner’s dilemma game. The enhancement effects are confirmed in the snowdrift game as well, which may imply that the facilitation effects show a high degree of universality independent of the structure of the applied spatial networks and the potential evolutionary game models. Our conclusion may be conducive to interpret the emergence and sustainability of cooperation within the structured populations.

Bitter taste: an agency agreement in Vietnam goes awry

Subject area The case authentically illustrates a common problem encountered within the business scope of an agent who is representing a European food ingredients manufacturer in an emerging market. The case describes the kind of legal set-up and contracts that are necessary to safeguard the long-term prospective of the business for both parties, the agent and overseas supplier. It explains what each party has to observe in case of a termination of the agency agreement. Study level/applicability This is a longitudinal case study of a market entry by a European food ingredients manufacturer through a foreign-owned third agent. The authors studied how sales developed over the first few years and then concentrated the investigation on the fact that after the sales volume was reached, the overseas manufacturer wants to cancel the agency agreement and do the business directly without getting the agent involved. Case overview This case describes and explains a common problem encountered frequently by overseas manufacturers who want to enter an emerging market through a third-party agent representation. The overseas supplier uses the agent’s service and solid reputation to enter an emerging market with limited exposure to costs and risk. The agent works towards guarding the relationship with the overseas supplier for as long as possible. The development of the relationship illustrates what kind of conditions have to be stipulated in advance to provide an acceptable solution to both parties concerned once they part ways. Expected learning outcomes This research is based on a European food ingredients manufacturer, who was expanding its business in different Asian emerging markets, namely, Vietnam and Cambodia. The agent was a long-time established trading house who acted frequently as agent for overseas companies that wanted to get a foothold in these promising Asian emerging markets. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 5: International Business

Preverbal infants affirm third-party interventions that protect victims from aggressors

Protective interventions by a third party on the behalf of others are generally admired, and as such are associated with our notions of morality, justice and heroism1–4. Indeed, stories involving such third-party interventions have pervaded popular culture throughout recorded human history, in myths, books and movies. The current developmental picture is that we begin to engage in this type of intervention by preschool age. For instance, 3-year-old children intervene in harmful interactions to protect victims from bullies5, and furthermore, not only punish wrongdoers but also give priority to helping the victim6. It remains unknown, however, when we begin to affirm such interventions performed by others. Here we reveal these developmental origins in 6- and 10-month old infants (N = 132). After watching aggressive interactions involving a third-party agent who either interfered or did not, 6-month-old infants preferred the former. Subsequent experiments confirmed the psychological processes underlying such choices: 6-month-olds regarded the interfering agent to be protecting the victim from the aggressor, but only older infants affirmed such an intervention after considering the intentions of the interfering agent. These findings shed light upon the developmental trajectory of perceiving, understanding and performing protective third-party interventions, suggesting that our admiration for and emphasis upon such acts — so prevalent in thousands of stories across human cultures — is rooted within the preverbal infant’s mind. Over six experiments, Kanakogi et al. show that infants as young as 6 months support third-party interventions that protect victims from aggressors. This suggests that human emphasis upon such acts is rooted within the preverbal infant’s mind.

Too much information? An experiment on communication and cooperation

There are many situations where an informed third-party agent communicates information about the value of a public good or group project to contributors. One example is the information provided by charity ratings organizations. I ask how information about the quality of a public good should be communicated in order to minimize free-riding. I propose that coarse or pooled messages about the quality of the public good can increase contributions and, in turn, social welfare. If individuals cannot distinguish between a public good that they would like to contribute to and a public good that would otherwise suffer from a high degree of free-riding, contributions to the latter may approach the efficient level of provision. Experimental results suggest that, in practice, some participants are averse to giving without having complete information and – as a result – coarse messages may decrease welfare.

Finding stable flows in multi-agent networks under an equal-sharing policy

A Multi-Agent network flow problem is addressed in this paper where a set of transportation-agents can control the capacities of a set of routes. Each agent incurs a cost proportional to the chosen capacity. A third-party agent, a customer, is interesting in maximizing the product flow transshipped from a source to a sink node through the network. He offers a reward proportional to the flow value, which is shared equally among the transportation-agents. We address the problem of finding a stable strategy (i.e., a Nash Equilibrium) that maximizes the network flow. In this paper, we present a Mixed Integer Linear Program (MILP) to model and solve this problem.

New advances in large‐scale distributed simulation and real‐time applications

New advances in large‐scale distributed simulation and real‐time applications

Modeling believable agents using a descriptive approach

Abstract The concept of believable agents in game and simulator design has become increasingly important. This is because the improved realism offered by synthetic agents can lead to the increased popularity and prolonged life of electronic games. This paper offers a model for the development of believable agents. Specifically, this paper proposes the use of a descriptive approach to agent design. Using this approach, in-game agents are designed to learn human emotional responses from real world data. To achieve this, training data was collected and fed to the descriptive learning believable agent model. Further, we conducted a comparative study to determine whether agents designed using this approach were more believable than agents designed using more traditional approaches. The findings from this study revealed that the descriptive learning agent was perceived by study participants to be more believable than the agent that was programmed according to the specifications of two third-party agent developers.

A Negotiation Framework for the Cloud Management System using Similarity and Gale Shapely Stable Matching Approach

One of the major issues in emerging cloud management system needs the efficient service level agreement negotiation framework, with an optimal negotiation strategy. Most researchers focus mainly on the atomic service negotiation model, with the assistance of the Agent Controller in the broker part to reduce the total negotiation time, and communication overhead to some extent. This research focuses mainly on composite service negotiation, to further minimize both the total negotiation time and communication overhead through the pre-request optimization of broker strategy. The main objective of this research work is to introduce an Automated Dynamic Service Level Agreement Negotiation Framework (ADSLANF), which consists of an Intelligent Third-party Broker for composite service negotiation between the consumer and the service provider. A broker consists of an Intelligent Third-party Broker Agent, Agent Controller and Additional Agent Controller for managing and controlling its negotiation strategy. The Intelligent third-party broker agent manages the composite service by assigning its atomic services to multiple Agent Controllers. Using the Additional Agent Controllers, the Agent Controllers manage the concurrent negotiation with multiple service providers. In this process, the total negotiation time value is reduced partially. Further, the negotiation strategy is optimized in two stages, viz., Classified Similarity Matching (CSM) approach, and the Truncated Negotiation Group Gale Shapely Stable Matching (TNGGSSM) approach, to minimize the communication overhead.

Provable Data Possession of Resource-constrained Mobile Devices in Cloud Computing

Benefited from cloud storage services, users can save their cost of buying expensive storage and application servers, as well as deploying and maintaining applications. Meanwhile they lost the physical control of their data. So effective methods are needed to verify the correctness of the data stored at cloud servers, which are the research issues the Provable Data Possession (PDP) faced. The most important features in PDP are: 1) supporting for public, unlimited numbers of times of verification; 2) supporting for dynamic data update; 3) efficiency of storage space and computing. In mobile cloud computing, mobile end-users also need the PDP service. However, the computing workloads and storage burden of client in existing PDP schemes are too heavy to be directly used by the resource-constrained mobile devices. To solve this problem, with the integration of the trusted computing technology, this paper proposes a novel public PDP scheme, in which the trusted third-party agent (TPA) takes over most of the calculations from the mobile end-users. By using bilinear signature and Merkle hash tree (MHT), the scheme aggregates the verification tokens of the data file into one small signature to reduce communication and storage burden. MHT is also helpful to support dynamic data update. In our framework, the mobile terminal devices only need to generate some secret keys and random numbers with the help of trusted platform model (TPM) chips, and the needed computing workload and storage space is fit for mobile devices. Our scheme realizes provable secure storage service for resource-constrained mobile devices in mobile cloud computing.

Multi-domain WLAN load balancing in WLAN/WPAN interference environments

The proliferation of wireless local area network (WLAN) deployments in enterprises, public areas, and homes will likely cause frequent geographical coverage overlap among multiple networks. A recent growing interest is the coordination among WLAN providers for efficient network management over a large coverage area. While radio resource management for a single WLAN has been studied extensively, little research work addresses resource management over multiple domains. When multiple WLANs co-locate in a small geographic vicinity, the lack of cooperative multi-domain resource management can cause significant performance degradation due to inter-domain interference. Unbalanced loads among multiple networks can incur congestion in a few WLANs while foregoing unused excess resources in others. Moreover, since WLANs often operate in unlicensed frequency bands shared by various public and private networks, they must cope with the dynamic RF environment involving a diverse set of interference sources. This paper introduces a new cooperative load balancing framework for multi-domain WLANs operating in an interference environment. A third-party-based architecture is proposed to facilitate fair radio resource allocation among multiple networks. The proposed third-party agent collects observed network state information from different WLANs and optimizes resource usage. Under the proposed scheme, resource utilization and co-channel interference can be adaptively balanced across the entire integrated system. The impact of other co-located interference sources in the operational environment are taken into account in the optimization process. The proposed load balancing framework for interference environments is a complement to the interference mitigation mechanisms operated at the PHY/MAC layer. Simulation results show that the proposed multi-domain load balancing scheme outperforms other schemes which do not consider interdomain interference or environmental interference.