Reduce Communication Overhead Research Articles

SEA: A Secure and Efficient Authentication and Authorization Architecture for IoT-Based Healthcare Using Smart Gateways

In this paper, a secure and efficient authentication and authorization architecture for IoT-based healthcare is developed. Security and privacy of patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. Secure authentication and authorization of a remote healthcare professional is the main focus of this work. Due to resource constraints of medical sensors, it is infeasible to utilize conventional cryptography in IoT-based healthcare. In addition, gateways in existing IoTs focus only on trivial tasks without alleviating the authentication and authorization challenges. In the presented architecture, authentication and authorization of a remote end-user is done by distributed smart e-health gateways to unburden the medical sensors from performing these tasks. The proposed architecture relies on the certificate-based DTLS handshake protocol as it is the main IP security solution for IoT. The proposed authentication and authorization architecture is tested by developing a prototype IoT-based healthcare system. The prototype is built of a Pandaboard, a TI SmartRF06 board and WiSMotes. The CC2538 module integrated into the TI board acts as a smart gateway and the WisMotes act as medical sensor nodes. The proposed architecture is more secure than a state-of-the-art centralized delegation-based architecture because it uses a more secure key management scheme between sensor nodes and the smart gateway. Furthermore, the impact of DoS attacks is reduced due to the distributed nature of the architecture. Our performance evaluation results show that compared to the delegation-based architecture, the proposed architecture reduces communication overhead by 26% and communication latency from the smart gateway to the end-user by 16%.

Mapping Adaptive Particle Filters to Heterogeneous Reconfigurable Systems

This article presents an approach for mapping real-time applications based on particle filters (PFs) to heterogeneous reconfigurable systems, which typically consist of multiple FPGAs and CPUs. A method is proposed to adapt the number of particles dynamically and to utilise runtime reconfigurability of FPGAs for reduced power and energy consumption. A data compression scheme is employed to reduce communication overhead between FPGAs and CPUs. A mobile robot localisation and tracking application is developed to illustrate our approach. Experimental results show that the proposed adaptive PF can reduce up to 99% of computation time. Using runtime reconfiguration, we achieve a 25% to 34% reduction in idle power. A 1U system with four FPGAs is up to 169 times faster than a single-core CPU and 41 times faster than a 1U CPU server with 12 cores. It is also estimated to be 3 times faster than a system with four GPUs.

Decentralised workflow scheduling in volunteer computing systems

Volunteer computing systems exploiting large amounts of geographically dispersed resources on the Internet for solving complex scientific problems. However, scheduling scientific workflows in a fully decentralised way and low overhead is a challenging task in these environments. To counter this challenge, this paper presents a fully decentralised proximity-aware workflow-scheduling policy for these environments. The proposed scheduling consists of three phases. In the first phase, each workflow application is partitioned into sub-workflows in order to minimise data dependencies among them. The second phase of the workflow-scheduling algorithm finds some resources to execute each sub-workflow. These resources are selected based on Quality of Service (QoS) constraints of the workflow, load balancing and proximity of resources. Each workflow can have QoS constraints in terms of minimum CPU speed and minimum RAM or hard disk requirements. In the third phase, sub-workflows will be executed on each resource based on local scheduling algorithm to minimise the partial makespan. The proposed scheduling policy focuses on the reduction of communication overhead to improve the performance of I/O-intensive and data-intensive workflows. Simulation results show that the proposed workflow-scheduling policy improves the average response time of scientific workflows up to 53.6% under a moderate load.

Data Aggregation Based on Saving Energy and Preserving Privacy in Wireless Sensor Networks

Privacy preserving plays an important role in application of the Internet of Things (IoT). As apart of the IT, Wireless Sensor Networks (WSNs) should provide the privacy preserving in data aggregation. This paper presents a novel energy-saving private-preserving aggregation scheme (ESPART) for Wireless Sensor Networks, which uses characteristic of the data aggregation tree structure to reduce communication overhead. It assigns the random time pieces to nodes with aim to avoid collision, and limits the scope of collusion data to reinforce data—loss resilience. Compared with the SMART algorithm have less communication overhead. ESPART uses data-dependent fusion characteristics of the tree structure to eliminate SMART algorithm for unnecessary communication node, so that in case of the same privacy protection security, the data transmission amount is reduced to about 48% of SMART.

Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-Grained Updates

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-a-service’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.

Environment orientation: a structured simulation approach for agent-based complex systems

Complex systems are collections of independent agents interacting with each other and with their environment to produce emergent behaviour. Agent-based computer simulation is one of the main ways of studying complex systems. A naive approach to such simulation can fare poorly, due to large communication overhead, and due to the scope for deadlock between the interacting agents sharing a computational platform. Agent interaction can instead be considered entirely from the point of view of the environment(s) within which the agents interact. Structuring a simulation using such Environment Orientation leads to a simulation that reduces communication overhead, that is effectively deadlock-free, and yet still behaves in the manner required. Additionally the Environment Orientation architecture eases the development of more sophisticated large-scale simulations, with multiple kinds of complex agents, situated in and interacting with multiple kinds of environments. We describe the Environment Orientation simulation architecture. We report on a number of experiments that demonstrate the effectiveness of the Environment Orientation approach: a simple flocking system, a flocking system with multiple sensory environments, and a flocking system in an external environment.

Data similarity aware dynamic node clustering in wireless sensor networks

Wireless Sensor Networks (WSNs) have been used by several kinds of urban and nature monitoring applications as an important interface between physical and computational environments. Node clustering is a common technique to organize data traffic, reduce communication overhead and enable better network traffic management, improving scalability and energy efficiency. Although current clustering protocols treat various kinds of dynamicity in the network, such as mobility or cluster-head rotations, few solutions consider the readings similarity, which could provide benefits in terms of better use of compression techniques and reactive detection of anomalous events. For maintaining similarity aware clusters, the synchronization of the cluster’s average reading would allow a distributed and adaptive operation. In this article, we propose an architecture for dynamic and distributed data-aware clustering, and the Dynamic Data-aware Firefly-based Clustering (DDFC) protocol to handle spatial similarity between node readings. The DDFC operation takes into account the biological principles of fireflies to ensure distributed synchronization of the clusters’ similar readings aggregations. DDFC was compared to other protocols and the results demonstrated its capability of maintaining synchronized cluster readings aggregations, thereby enabling nodes to be dynamically clustered according to their readings.

A Low-Stretch-Guaranteed and Lightweight Geographic Routing Protocol for Large-Scale Wireless Sensor Networks

Geographic routing is well suited for large-scale wireless sensor networks (WSNs) because it is nearly stateless. One important challenge is that network holes may arbitrarily increase the routing path length. Fortunately, recent studies have shown that constant path stretch is achievable using nonlocal information. The constant stretch, however, is possible at the cost of high communication and storage overhead: a source node must complete a “path-setup” process prior to data transmission by exchanging a message with a destination node using a default geographic routing (e.g., GPSR). In this article, we propose the first geographic routing protocol (LVGR) that provably achieves worst-case stretch of Θ (D/γ) (where D is the diameter of the network and γ is the communication range of nodes) with low communication and storage overhead . LVGR represents a hole as a convex hull, the internal structure of which is represented as a local visibility graph . Based on the convex hulls and local visibility graphs, LVGR generates paths with guaranteed stretch. Through theoretical analysis and extensive simulations, we prove the worst-case stretch of LVGR and demonstrate that LVGR reduces communication overhead by up to 97% and storage overhead by up to 60%, compared with the state of the art.

DJAO: A Communication-Constrained DCOP Algorithm that Combines Features of ADOPT and Action-GDL

In this paper we propose a novel DCOP algorithm, called DJAO, that is able toefficiently find a solution with low communication overhead; this algorithm can be used for optimal and bounded approximate solutions by appropriately setting the error bounds. Our approach builds on distributed junction trees used in Action-GDL to represent independence relationsamong variables. We construct an AND/OR search space based on these junction trees.This new type of search space results in higher degrees for each OR node, consequently yielding a more efficient search graph in the distributed settings. DJAO uses a branch-and-bound search algorithm to distributedly find solutions within this search graph. We introduce heuristics to compute the upper and lower boundestimates that the search starts with, which is integral to our approach for reducing communication overhead. We empirically evaluate our approach in various settings.

Local interactions over global broadcasts for improved task allocation in self-organized multi-robot systems

We present a study of self-organized multi-robot task-allocation, examining performance under local and centralized communication strategies. The results extend our current understanding of the effects of communication by providing evidence that local strategies can improve system performance over centralized strategies, in terms of total task throughput as well as reduced communication overheads. The framework employed is the attractive field model, a generic model of self-organized division of labour derived from observations of ant, human and robot social systems. The framework provides sufficient abstraction to accommodate both communication strategies. Each of the studies used 16 e-puck robots in a simplified manufacturing environment where sensing and communication was realized using camera-based overhead tracking and centralized communication. In terms of task throughput, communication overhead and energy efficiency, the experimental results show that systems with restricted access to information perform better than systems with free flow of information. This suggests a potential paradigm shift where, for self-organizing systems, diminishing access to information renders a system more efficient.

Harnessing coherence of area decomposition and semantic shared spaces for task allocation in a robotic fleet

Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission. A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task. This paper presents the work that harnesses an area decomposition algorithm, and a space-based middleware to facilitate task allocation process in unstructured and dynamic environments. To reduce spatial interference between robots, area decomposition algorithm divides a working area into cells which are then dynamically assigned to robots. In addition, coordination and collaboration among distributed robots are realized through a space-based middleware. For this purpose, the space-based middleware is extended with a semantic model of robot capabilities to improve task selection in terms of flexibility, scalability, and reduced communication overhead during task allocation. In this way a framework which exploits the synergy of area decomposition and semantically enriched space-based approach is created. We conducted performance tests in a specific precision agriculture use case focusing on the utilization of a robotic fleet for weed control introduced in the European Project RHEA – Robot Fleets for Highly Effective Agriculture and Forestry Management.

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.

An Improved Byzantine Fault-tolerant Program for WSNs

In order to increase the level of fault-tolerance of wireless sensor networks, thus to enhance the reliability and accuracy, we studies the traditional byzantine fault-tolerant program, and makes improvements in the environment of WSNs, reducing the number of rounds of message exchange between network nodes, thus improving the efficiency and reducing communication overhead and energy consumption. The simulation result shows that our program makes all normal network nodes reach an agreement, while the number of rounds of message exchange greatly decreases compared to the traditional byzantine program.

High-Accuracy and Privacy-Preserving Oriented Data Aggregation Algorithm in Sensor Networks

Wireless sensor networks(WSNs) is an important part of Internet of Things(IoT).Privacy-preserving in data aggregating is an effective way to protect security of data in WSN.SMART is one of data aggregation algorithms.This paper presents some optimizing factors used to enhance performance of SMART scheme in terms of privacy-preserving and data aggregating precision.Based on the optimal parameters,it proposes some new data aggregating schemes,which reduce communication overhead and then the energy consumption.Therefore,they prolong the lifetime of the network.Simulation results show that the proposed schemes P-SMART-CLPNT demonstrate good performance in data aggregation precision,communication overhead,and privacy-preserving with the optimal parameters.

EVALUATING THE PERFORMANCE OF THE LOCATION-AIDED ROUTING-1P ROUTE DISCOVERY ALGORITHM

Dynamic Routing Protocols (DRPs) are widely-used for routing information among mobile nodes in Mobile Ad Hoc Network (MANET) and establish and maintain connectivity within the network. A DRP comprises two main phases: route discovery and route maintenance. The route discovery phase involves transmission of large number of redundant control packets consuming significant portion of the nodes power and increase communication overheads. Recently, a new efficient and effective route discovery algorithm has been developed, namely, the LAR-1P algorithm, which combines two well-known routing protocols; these are: the Location-Aided Routing scheme 1 (LAR-1) and probabilistic algorithms. This study evaluates and compared the performance of the LAR-1P algorithm against the performance of a number of route discovery algorithms through simulation. For each simulation, the number of retransmissions and reachability are estimated and compared. The simulations results demonstrated that LAR-1P provides better performance than all other algorithms it is compared with, as it significantly reduces communication overheads while maintaining almost the same network connectivity.

A Novel Distributed Jointly Sparse Optimization Algorithm

This paper develops a novel distributed jointly sparse optimization algorithm to recover the sparse signal, in which the joint sparse structure is used to improve the quality of recovery. In distributed networked multi-agent system, each agent collects measurement vectors and aims to recover its own sparse signal collaboratively in a distributed manner. We propose to use the factored gradient approach to calculate the solution iteratively, and introduce a energy vector of the current estimates as the consensus constrain which is updated by inexact consensus optimization for its distributed implementation. This algorithm does not require excessive data transmissions from distributed agents to fusion center, which reduces communication overhead and the computational complexity of the agents. Simulation results demonstrate that the proposed distributed algorithm has strong recovery performance and can reach global convergence as well as fast convergence rate.

A Novel Distributed Jointly Sparse Optimization Algorithm

This paper develops a novel distributed jointly sparse optimization algorithm to recover the sparse signal, in which the joint sparse structure is used to improve the quality of recovery. In distributed networked multi-agent system, each agent collects measurement vectors and aims to recover its own sparse signal collaboratively in a distributed manner. We propose to use the factored gradient approach to calculate the solution iteratively, and introduce a energy vector of the current estimates as the consensus constrain which is updated by inexact consensus optimization for its distributed implementation. This algorithm does not require excessive data transmissions from distributed agents to fusion center, which reduces communication overhead and the computational complexity of the agents. Simulation results demonstrate that the proposed distributed algorithm has strong recovery performance and can reach global convergence as well as fast convergence rate.

An Efficient Confidentiality and Integrity Preserving Aggregation Protocol in Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of sensor nodes with limited energy which is difficult to replenish. In-network data aggregation is the main solution to minimize energy consumption and maximize network lifetime by reducing communication overhead. However, performing data aggregation while preserving data confidentiality and integrity is challenging, because adversaries can eavesdrop and modify the aggregation results easily by compromised aggregation nodes. In this paper, we propose an efficient confidentiality and integrity preserving aggregation protocol (ECIPAP) based on homomorphic encryption and result-checking mechanism. We also implement ECIPAP on SimpleWSN nodes running TinyOS. Security and performance analysis show that our protocol is quite efficient while persevering both aggregation confidentiality and integrity.

Location-Based Distributed Group Key Agreement Scheme for Vehicular Ad Hoc Network

With the rapid development of vehicular ad hoc networks (VANETs), security issues are concerned. Among various security measures, secure group key agreement for VANETs attracts more and more attention. Researchers have studied generic group key agreement schemes for a long while; however, most of them are not designed for VANETs. Considering energy-constrained and fast moving features in vehicular ad hoc network, we propose a new location-based distributed group key agreement LDGKA scheme for VANETs. We adopt a hybrid approach in which members in the vehicular ad hoc network form various logic groups in the same region. Within each group, virtual key tree model is employed so that the rekeying operation can be efficiently carried out when members leave or join. It only requires to rebuild [Formula: see text] keys when one node leaves or joins. Moreover, we adopt one-way key derivation to rebuild keys in order to reduce communication overhead of distributing new keys. We also design a protocol for establishing the secure temporary channel dynamically for the nodes that are located in different regions.

Applying transmission-coverage algorithms for secure geocasting in VANETs

Existing geocasting algorithms for VANETs provide either high availability or security, but fail to achieve both together. Most of the privacy preserving algorithms for VANETs have low availability and involve high communication and computation overheads. The reliable protocols do not guarantee secrecy and privacy. We propose a secure, privacy-preserving geocasting algorithm for VANETs, which uses direction-based dissemination. Privacy and security are achieved using public key encryption and authentication and pseudonyms. To reduce communication overheads resulting from duplication of messages, we adapt a transmission-coverage algorithm used in mobile sensor networks, where nodes delay forwarding messages based on its uncovered transmission perimeter after neighbouring nodes have broadcast the message. Our analysis shows that our protocol achieves a high delivery rate, with reasonable computation and communication overheads.