Message Relay Research Articles

Enabling resilient UAV swarms through multi-hop wireless communications

In the last decade, the popularity of UAVs, colloquially known as drones, has increased tremendously. Nowadays, drones are used in a wide range of use-cases, such as precision agriculture, surveillance, and photography. Many of these use-cases can be made more efficient if multiple UAVs are used cooperatively (i.e., in a swarm). To achieve this, communication between the UAVs is paramount. To ensure communication, many works rely on the existing infrastructure (e.g., 4G). However, in many rural areas, this infrastructure does not exist. In those cases, an ad hoc (Wi-Fi) network is the most adequate alternative. Yet, due to the limited communication range of Wi-Fi, it is not possible to let UAVs in a swarm to communicate over a long distance. To solve this issue a relay approach is necessary. Despite general solutions to relay messages between (mobile) nodes already exist, many UAV swarms rely on master–slave communication. Thus, a specific solution for this type of communication might be more efficient. Hence, in this work, we propose a strategy to efficiently relay messages for UAV swarms adopting the master–slave communication paradigm. Our approach seeks to introduce a very small message overhead to avoid congestion of the network, and to provide more bandwidth for the actual applications of the UAV swarm. We tested our approach using a realistic UAV simulator called ArduSim. Our results show that our approach is capable of detecting all the nodes in the network within a few seconds. Furthermore, we applied our message relay approach on an existing swarm application (where a swarm of UAVs had to follow a mission), and our results show that, now, the communication range of the UAVs can be much larger, without impacting other aspects of the mission (such as flight time).

Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos

This research presents a novel method to assess inter-chain message relay time in the Cosmos blockchain network, which employs the Inter-Blockchain Communication (IBC) protocol for blockchain interoperability. While inter-chain transactions in Cosmos lag behind intra-chain transactions, this research conducts a thorough performance evaluation, emphasizing message relay time across diverse Cosmos chains. The results show a strong association between inter-chain transaction frequency and overall transaction processing speed (TPS), underscoring the inherent trade-off between scalability and compatibility in inter-chain communication protocols. A thorough understanding of inter-chain transaction performance is essential for advancing interoperability and improving cross-blockchain network designs. As a result, this research improves our understanding of Cosmos’ functionality and provides insightful recommendations for boosting the effectiveness and scalability of inter-chain communication. The study also sheds light on message delivery patterns in the Cosmos ecosystem, showing that IBC message relay time has an average duration of 55.448 s and is distributed lognormally. Notably, the time between the commitment of the IBC RecvPacket transaction and the commitment of the IBC Acknowledgement transaction (R to A time) considerably impacts the effectiveness of message transmission, which causes delays in the IBC message relay process.

Multi-UAV Relay Connectivity Optimization for Heterogeneous Users Based on Load Balancing and Throughput Maximization

By relaying messages between users, unmanned aerial vehicles (UAVs), which are frequently utilized in the communication industry, can be used as airborne communication relays to resolve connectivity problems. In order to effectively connect a large number of heterogeneous users dispersed on the ground, we deploy a large number of UAVs for reliable relay coverage and message transmission between users. We initially put forth a relay coverage algorithm that continuously optimizes the position of UAVs and the cell division of terrestrial users in order to provide users with full coverage. Second, we study several relay message forwarding methods between UAVs while taking into account the features of heterogeneous users. A relay selection method, which takes the relay link throughput as the optimization goal, is proposed to ensure full connectivity between UAVs. Finally, we contrast and analyze how various relay forwarding methods affect the effectiveness of the relay network. The experimental results demonstrate that the proposed relay coverage algorithm can balance the UAV relay load better and reduce transmission delay, while the suggested relay selection strategy can minimize the number of isolated UAVs and maximize the throughput of UAV relay links.

EsECC_SDN: Attack Detection and Classification Model for MANET

Mobile Ad Hoc Networks (MANET) is the framework for social networking with a realistic framework. In the MANET environment, based on the query, information is transmitted between the sender and receiver. In the MANET network, the nodes within the communication range are involved in data transmission. Even the nodes that lie outside of the communication range are involved in the transmission of relay messages. However, due to the openness and frequent mobility of nodes, they are subjected to the vast range of security threats in MANET. Hence, it is necessary to develop an appropriate security mechanism for the data MANET environment for data transmission. This paper proposed a security framework for the MANET network signature escrow scheme. The proposed framework uses the centralised Software Defined Network (SDN) with an ECC cryptographic technique. The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN (EsECC_SDN) for attack detection and classification. The developed EsECC-SDN was adopted in two stages for attack classification and detection: (1) to perform secure data transmission between nodes SDN performs encryption and decryption of the data; and (2) to detect and classifies the attack in the MANET hyper alert based Hidden Markov Model Transductive Deep Learning. Furthermore, the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database (DB). The escrow handles these processes, and attacks are evaluated using the hyper alert. The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model. The proposed model uses the CICIDS dataset for attack detection and classification. The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost, Regression, and Decision Tree. The performance of the proposed EsECC_SDN exhibits ∼3% improved performance compared with conventional techniques.

Blossom: Cluster-Based Routing for Preserving Privacy in Opportunistic Networks

Opportunistic networks are an enabler technology for typologies without centralized infrastructure. Portable devices, such as wearable and embedded mobile systems, send relay messages to the communication range devices. One of the most critical challenges is to find the optimal route in these networks while at the same time preserving privacy for the participants of the network. Addressing this challenge, we presented a novel routing algorithm based on device clusters, reducing the overall message load and increasing network performance. At the same time, possibly identifying information of network nodes is eliminated by cloaking to meet privacy requirements. We evaluated our routing algorithm in terms of efficiency and privacy in opportunistic networks of traditional and structured cities, i.e., Venice and San Francisco by comparing our approach against the PRoPHET, First Contact, and Epidemic routing algorithms. In the San Francisco and Venice scenarios, Blossom improves messages delivery probability and outperforms PRoPHET, First Contact, and Epidemic by 46%, 100%, and 160% and by 67%, 78%, and 204%, respectively. In addition, the dropped messages probability in Blossom decreased 83% compared to PRoPHET and Epidemic in San Francisco and 91% compared to PRoPHET and Epidemic in Venice. Due to the small number of messages generated, the network overhead in this algorithm is close to zero. The network overhead can be significantly reduced by clustering while maintaining a reliable message delivery.

Idiopathic progressive polyneuropathy presented with frequently acute pulmonary oedema: a case report

Neuropathy is when nerve damage interferes with the functioning of the peripheral nervous system (PNS). When the cause can’t be determined, it’s called idiopathic neuropathy(Idiopathic neuropathy, now designated as chronic idiopathic axonal polyneuropathy (CIAP). There are three kinds of nerves within the PNS. Sensory nerves relay messages from the sense organs to the brain. This allows sensations of temperature and touch. Motor nerves transmit signals from the brain to the muscles. This helps the brain control the muscles. Autonomic nerves control body functions like heart rate, breathing, and digestion. Damage to nerve cells can affect how the PNS communicates with the rest of the body. Symptoms can include numbness, pain, and balance issues. It’s called acute neuropathy when symptoms develop suddenly. Alternately, it’s called chronic neuropathy when symptoms start slowly and increase over time. Diagnosis involves physical examination and review of medical history. Diagnostic testing may include blood tests, nerve testing, and imaging tests. There is no cure for idiopathic neuropathy. Treatments including medication, physical therapy, and lifestyle modifications can help you function and feel better. We report a case of idiopathic polyneuropathy presented with frequent acute pulmonary edema for a year.

Towards reinforcement learning in UAV relay for anti-jamming maritime communications

Maritime communications with sea surface reflections and sea wave occlusions are susceptible to jamming attacks due to the wide geographical area and intensive wireless communication services. Unmanned Aerial Vehicles (UAVs) help relay messages to improve communication performance, but the relay policy that depends on the rapidly changing maritime environments is difficult to optimize. In this paper, a reinforcement learning-based UAV relay policy for maritime communications is proposed to resist jamming attacks. Based on previous transmission performance, the relay location, the received power of the transmitted signal and the received jamming power, this scheme optimizes the UAV trajectory and relay power to save the energy consumption and decrease the Bit-Error-Rate (BER) of the maritime signals. A deep reinforcement learning-based scheme is also proposed, which designs a deep neural network with dueling architecture to further improve the communication performance and computational complexity. The performance bounds regarding the signal to interference plus noise ratio, energy consumption and the communication utility are provided based on the Nash equilibrium of the game against jamming, and the computational complexity of the proposed schemes is analyzed. Simulation results show that the proposed schemes improve the energy efficiency and decrease the BER compared with the benchmark.

Improving Traffic Load Distribution Fairness in Mobile Social Networks

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.

Nation-Statist Soteriology and Traditions of Defeat: Religious-Zionism, the Ninth of Av, and Jerusalem Day

AbstractWe ask how the theopolitics of a nation-state, and especially its soteriology, engage with traditions that preceded the state and relay messages that contradict this theopolitics. To discuss this question, we address the evolving (re-)interpretation of the Ninth of Av—a ritual commemoration of the destruction of Jerusalem and the end of Jewish (Judean) self-rule in ancient times—by Religious-Zionist commentators. We further compare this interpretation to the Religious-Zionist appropriation of Jerusalem Day, a civic holiday celebrating the establishment of Israeli control over East Jerusalem in the June 1967 war. We argue that the statist imperative of the superiority of nation-statist theopolitics suggests that traditions are co-opted to fit in with its soteriology, with varying degrees of resistance or willing accommodation by carriers of these traditions. This co-opting may result in either the de-politicization of what the statist view would see as religion or the religionization of the state's own civic and so-called secular holidays.

A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks

With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity networks (Time-varying Relationship Groups, TVRGs). Firstly, we construct the relationship groups based on the time-varying characteristics according to the intimacy between users. Secondly, we calculate the importance of nodes by their connectivity time and communication frequency. Finally, we find the suitable message relay nodes according to the similarity of node weights and their action trajectories and design the routing query algorithm accordingly. The simulation results show that the algorithm can vastly improve the message query success rate, effectively improve the data transmission efficiency, and reduce the average delay and system overhead compared with the existing routing algorithms.

Cloud-based platforms for LoRa internet of things: a survey

<span><span>Internet of Things</span> (IoT) establishes a smart communication system between the human being and the surrounding objects (or “things”) while using the internet as the backbone of this system. Cloud is the fundamental constituent of IoT. It offers important application services in numerous <span>application domains</span>. Indeed, many IoT cloud platforms are competing to provide appropriate and very specific IoT services. The LoRa platform in particular presents a long-range, low-bit rate, low-power, wireless telecommunication protocol for IoT. As such, end-devices make use of LoRa through a sole wireless hop in order to communicate to the gateway(s) that is (are) considered as relay messages and transparent bridges between these end-devices and an IoT cloud while connecting to the Internet. This article surveys the most used IoT <span>cloud platforms</span> suitable for the LoRa communication protocol through a deep and detailed study of previously published works related to this topic. Moreover, a comparison is made between these IoT cloud platforms according to their characteristics and application domains. The overall aim of the present paper is to provide researchers in the field with detailed knowledge concerning the IoT <span>cloud platforms appropriate to the LoRa communication system </span>including the advantages and drawbacks of each one of them.</span>

TSIRP: A Temporal Social Interactions-Based Routing Protocol in Opportunistic Mobile Social Networks

With the increasing number of smart device users, data transmission between users is becoming more important, and a network architecture called opportunistic mobile social network (OMSN) is gaining attention. However, routing in OMSNs is a challenging problem due to the frequent disconnection between nodes and the absence of paths from the source to the destination. It results in a complex topology and a low packet transmission success rate. Therefore, we propose a novel routing algorithm called the temporal social interactions-based routing protocol (TSIRP) for solving the problem of low network performance due to the improper selection of message relay nodes. First, we focus on the temporal context of social interactions. Specifically, at a certain time of the day, a person has specific people with whom the person usually interacts (e.g., workers usually meet co-workers during working hours; students usually meet their classmates during class). Based on temporal social interactions between nodes, potential forwarding metrics are proposed and calculated for each time of the day to make forwarding decisions. Second, we propose a new scheme to control the message spreading rate, which allows achieving a balance between delivery latency and overhead ratio. In addition, an analytical model is also designed using an absorbing Markov chain to estimate the performance of TSIRP. Simulations were also conducted, and the results indicate that TSIRP can achieve better performance than existing routing protocols in terms of packet delivery ratio, delivery latency, network overhead ratio, and average hop count.

Tuning tuft cells: new ligands and effector functions reveal tissue-specific function.

Tuft cells are rare chemosensory epithelial cells that monitor their environment and relay messages to the surrounding tissue via secretion of neuromodulatory and immunomodulatory molecules. In the small intestine tuft cells detect helminth infection, protist colonization, and bacterial dysbiosis, and initiate a type 2 immune response characterized by tissue remodeling. In the airways, tuft cells sense bacteria, allergens, and noxious stimuli and drive evasive behavior, neuroinflammation, and anti-bacterial responses. Here we summarize the most recent tuft cell research and discuss how these findings have provided insight into tuft cell diversity. Built around a core program of chemosensing, tuft cell receptors and effector functions are tuned to the unique environmental exposure and physiology of their surrounding tissue.

EVC-TDMA: An enhanced TDMA based cooperative MAC protocol for vehicular networks

In order to optimize the communication mechanism in the vehicular adHoc networks (VANETs), this paper presents an enhanced TDMA based cooperative MAC protocol called EVC-TDMA. In EVC-TDMA, when the vehicles need to transmit multi-hop messages, they choose relay nodes dynamically according to the relative speeds and buffer lengths. By monitoring the broadcasting messages, the other nodes can know the buffer lengths of the relay nodes, they will help transmit the relay messages when their buffer are idle or less. Considering the data arrive follows a Poison Point Process, this paper analyzes the nodes' buffer length based on a two-dimension Markov process. The simulation results show that EVC-TDMA can reduce the dropping rate and improve the throughput of the network.

On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things

In this article, we investigate the jointly optimized resource allocation with hybrid multiple access in energy-efficient industrial Internet of Things (IIoT), where some devices (e.g., those for critical control devices) have higher transmission priority and stable energy supply while some devices (e.g., those for comprehensive sensors) may not. We consider a system model supporting wireless powered IIoT devices, with certain user terminal as a potential relay for the transmission between a hybrid access point and another user terminal. Constrained by the limited energy storage, the user needs to harvest energy before relaying and only the harvested energy is utilized for the following transmission. We propose a collaborative orthogonal and nonorthogonal multiple access protocol where two cooperation schemes with and without decoding the relay message are applied. Jointly considering time sharing in the transmission process, power splitting for simultaneous wireless information and power transfer, and transmit power allocation at the cooperative user, the achievable rate regions under the Rayleigh fading channel model are derived. Based on which, an optimization problem on resource allocation strategies is formulated and discussed. Both analytical and numerical results are provided, illustrating the impact of user geometry on the achievable rates as well as the optimal resource allocation with different cooperative strategies applied in different use cases. Aiming to enhance resource utilization, energy-efficient cooperation enables the combination of various transmission modes and networking classes in large scale networks, as well as a better use of ambient radio frequency signals for wireless powered transmissions.

A routing strategy with energy optimisation based on community in mobile social networks

The paper proposes an optimal energy efficiency routing strategy minimised network energy consumption with a given delay based on community. Firstly, the expected energy consumption and delay of message delivery are obtained through Markov chain. Then a cost function combined with energy consumption and delay for message deliver is designed. Thus we obtain the optimisation function for delivering message of relay to comprehensive cost. Further, the reward function of relay is given. Finally, the optimal expected reward of optimal relay is achieved using the optimal stopping theory for realising the optimal energy efficiency routing strategy. In simulations, the average energy consumption, the average delay and the average delivery ratio of routing optimisation strategy are compared with other routing strategies in related literatures. The results show the proposed strategy has smaller average energy consumption, smaller average delay and bigger average delivery ratio, and better energy consumption optimisation results can be achieved.

A routing strategy with energy optimisation based on community in mobile social networks

The paper proposes an optimal energy efficiency routing strategy minimised network energy consumption with a given delay based on community. Firstly, the expected energy consumption and delay of message delivery are obtained through Markov chain. Then a cost function combined with energy consumption and delay for message deliver is designed. Thus we obtain the optimisation function for delivering message of relay to comprehensive cost. Further, the reward function of relay is given. Finally, the optimal expected reward of optimal relay is achieved using the optimal stopping theory for realising the optimal energy efficiency routing strategy. In simulations, the average energy consumption, the average delay and the average delivery ratio of routing optimisation strategy are compared with other routing strategies in related literatures. The results show the proposed strategy has smaller average energy consumption, smaller average delay and bigger average delivery ratio, and better energy consumption optimisation results can be achieved.

Securing smart vehicles from relay attacks using machine learning

Due to the rapid developments in intelligent transportation systems, modern vehicles have turned into intelligent transportation means which are able to exchange data through various communication protocols. Today’s vehicles portray best example of a cyber-physical system because of their integration of computational components and physical systems. As the IoT and data remain intrinsically linked together, the evolving nature of the transportation network comes with a risk of virtual vehicle hijacking. In this paper, we propose a combination of machine learning techniques to mitigate the relay attacks on Passive Keyless Entry and Start (PKES) systems. The proposed algorithm uses a set of key fob features that accurately profiles the PKES system and a set of driving features to identify the driver. First relay attack detection is performed, and if a relay attack is not detected, the vehicle is unlocked and algorithm proceeds to gain the driving features and use neural networks to identify whether the current driver is whom he/she claims to be. To assess the machine learning model, we compared the decision tree, SVM, and KNN method using a three-month log of a PKES system. Our test results confirm the effectiveness of the proposed method in recognizing relayed messages. The proposed methods achieve 99.8% accuracy rate. We used a Long Short-Term Memory recurrent neural network for driver identification based on the real-world driving data, which are collected from a driver who drives the vehicles on several routes in real-world traffic conditions.

Two-Way Secure Communication with Multiple Untrusted Half-Duplex AF Relays

In this paper, we analyze the secrecy outage probability (SOP) of a cognitive cooperative radio network in a two-way communication in which two secondary source communicate with each other via multiple untrusted half-duplex amplify and forward relays in the absence of direct link. Due to the cognitive scenario, power is allocated to secondary nodes on the basis of outage constraint of the primary network. In the absence of direct link between two sources, communication completes in two time slots. In the first time slot, both of the sources broadcast the information signal and in the second time slot, a selected relay broadcasts the amplified information signals of both of the sources. Relays being untrusted, they can eavesdrop the message from the information signal. A particular relay, which maximizes the end-to-end secrecy capacity, is selected to broadcast the signal. The selected untrusted relay can only eavesdrop the message and other relays forcefully remain in idle condition. At the untrusted relay, information signals of both of the sources act as a jamming to each other. The selected untrusted relay harvests the energy using a power splitting ratio scheme. We observe the performance of the proposed model in terms of SOP. We find the optimal values of energy harvesting factor at which SOP becomes minimum. Several important parameters such as the impact of number of untrusted relays, primary transmit power, peak transmit power of secondary sources, threshold outage rate of primary receiver and threshold secrecy rate on SOP is indicated. An analytical expression for the SOP has been developed in a single integration form. Numerical results based on analytical expression are verified by MATLAB simulation.

Voices off: Stanley Milgram's cyranoids in historical context.

This article revisits a forgotten, late project by the social psychologist Stanley Milgram: the ‘cyranoid’ studies he conducted from 1977 to 1984. These investigations, inspired by the play Cyrano de Bergerac, explored how individuals often fail to notice when others do not speak their own thoughts, but instead relay messages from a hidden source. We situate these experiments amidst the intellectual, cultural, and political concerns of late Cold War America, and show how Milgram’s studies pulled together a variety of ideas, anxieties, and interests that were prevalent at that time and have returned in new guises since. In discussing the cyranoid project’s background and afterlife, we argue that its strikingly equivocal quality has lent itself to multiple reinterpretations by historians, psychologists, performers, artists, and others. Our purpose is neither to champion Milgram’s work nor to amplify the critiques already made of his methods. Rather, it is to consider the uncertain, allusive, and elusive aspects of the cyranoid project, and to seek to place that project in context, whilst asking where ‘context’ might end. We show how the experiments’ range of meanings, in different temporal registers, far exceeded the explanatory rubric that Milgram and his intellectual critics provided at that time, and ponder the risk for the historian of making anachronistic or teleological assumptions. In short, we argue, cyranoids invite our open-ended exploration of ‘voices offstage’ in social and psychological relations, and offer a useful tool for thinking about historical context and the nature of historical interpretations.