Multi-Parametric Clustering for Sensor Node Coordination in Cognitive Wireless Sensor Networks

The deployment of wireless sensor networks for healthcare applications have been motivated and driven by the increasing demand for real-time monitoring of patients in hospital and large disaster response environments. A major challenge in developing such sensor networks is the need for coordinating a large number of randomly deployed sensor nodes. In this study, we propose a multi-parametric clustering scheme designed to aid in the coordination of sensor nodes within cognitive wireless sensor networks. In the proposed scheme, sensor nodes are clustered together based on similar network behaviour across multiple network parameters, such as channel availability, interference characteristics, and topological characteristics, followed by mechanisms for forming, joining and switching clusters. Extensive performance evaluation is conducted to study the impact on important factors such as clustering overhead, cluster joining estimation error, interference probability, as well as probability of reclustering. Results show that the proposed clustering scheme can be an excellent candidate for use in large scale cognitive wireless sensor network deployments with high dynamics.     

Lifetime extension based on residual energy for receiver-driven multi-hop wireless network

An important research topic in wireless sensor networking is the extension of operating time by controlling the power consumption of individual nodes. In a receiver-driven communication protocol, a receiver node periodically transmits its ID to the sender node, and in response the sender node sends an acknowledgment, after which data transmission starts. By applying such a receiver-driven protocol to wireless sensor networks, the average power consumption of the network can be controlled, but there still remains the problem of unbalanced load distribution among nodes. Therefore, part of the network shuts down when the battery of the node that consumes the most power is completely discharged. To extend the network lifetime, we propose a method where information about the residual energy level is exchanged through ID packets in order to balance power consumption. Simulation results show that the network lifetime can be extended by about 70–100 % while maintaining high network performance in terms of packet collection ratio and delay.     

Step to improve neural cryptography against flipping attacks

Synchronization of neural networks by mutual learning has been demonstrated to be possible for constructing key exchange protocol over public channel. However, the neural cryptography schemes presented so far are not the securest under regular flipping attack (RFA) and are completely insecure under majority flipping attack (MFA). We propose a scheme by splitting the mutual information and the training process to improve the security of neural cryptosystem against flipping attacks. Both analytical and simulation results show that the success probability of RFA on the proposed scheme can be decreased to the level of brute force attack (BFA) and the success probability of MFA still decays exponentially with the weights' level L. The synchronization time of the parties also remains polynomial with L. Moreover, we analyze the security under an advanced flipping attack.     

PPAS: privacy protection authentication scheme for VANET

Vehicular ad hoc network (VANET) can increase the traffic efficiency by allowing arbitrary vehicles to broadcast the messages to other vehicles and road side units (RSUs). But due to the openness of the wireless network, VANET is very vulnerable to forgery attack. Thus, the security and privacy of the messages should be provided, to make sure that the real identity of vehicles can be traceable by authorized party while not be revealed to other vehicles. The existing solutions can neither satisfy the privacy requirement nor have an effective message verification scheme on vehicles. A secure and privacy protect authentication scheme is proposed in this paper, which comprises local authentication and roaming authentication for VANET based on bilinear pairing and can provide secure communications and anonymous authentication among RSUs and vehicles.     

A Novel LTE Scheduling Algorithm for Green Technology in Smart Grid

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.     

An after-shannon measure of the storage capacity of an associative noise-like coding memory

The maximum amount of information that can be stored, on the average, in each storage element, according to an associative scheme, has been measured for the memory model proposed by the author (Bottini 1980). In this model, the (binary) items being stored are coded by noise-like keys and the memory traces formed in this way are superimposed, by algebraic addition, on the same many-level storage elements. It is shown that the problem of measuring the information retrieved from the memory in a single recall and the problem — concerning the data-communication field —of measuring the information transmitted over a noisy channel are formally similar. In particular, the Shannon noisy-channel coding theorem can find an application also in our case of an associative memory. Finally, it is evidenced that the so-called matrix model of an associative memory has the same storage capacity as the model studied here.     

SHER: A Colored Petri Net Based Random Mobility Model for Wireless Communications

In wireless network research, simulation is the most imperative technique to investigate the network’s behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER), a novel two-dimensional (2-D) Colored Petri net (CPN) based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and rectify the errors of our proposed model.     

Contention-Aware Communication Schedule for High-Speed Communication

A lot of efforts have been devoted to address the software overhead problem in the past decade, which is known as the major hindrance on high-speed communication. However, this paper shows that having a low-latency communication system does not guarantee to achieve high performance, as there are other communication issues that have not been fully addressed by the use of low-latency communication, such as contention and scheduling of communication events. In this paper, we use the complete exchange operation as a case study to show that with careful design of communication schedules, we can achieve efficient communication as well as prevent congestion. We have developed a complete exchange algorithm, the Synchronous Shuffle Exchange, which is an optimal algorithm on the non-blocking network. To avoid congestion loss caused by the non-deterministic delays in communication events, a global congestion control scheme is introduced. This scheme coordinates all participating nodes to monitor and regulate the traffic load, which effectively avoids congestion loss and maintains sufficient throughput to maximize the performance. To improve the effectiveness of the congestion control scheme when working on the hierarchical network, we incorporate information on the network topology to devise a contention-aware permutation. This permutation scheme generates a communication schedule, which is both node and switch contention-free as well as distributing the network loads more evenly across the hierarchy. This relieves the congestion build-up at the uplink ports and improves the synchronism of the traffic information exchange between cluster nodes. Performance results of our implementation on a 32-node cluster with various network configurations are examined and reported in this paper.     

Efficient public verification proof of retrievability scheme in cloud

Cloud storage is an important service of cloud computing. After data file is outsourced, data owner no longer physical controls over the storage. To efficiently verify these data integrity, several Proof of Retrievability (POR) schemes were proposed to achieve data integrity checking. The existing POR schemes offer decent solutions to address various practical issues, however, they either have a non-trivial (linear or quadratic) communication cost, or only support private verification. And most of the existing POR schemes exist active attack and information leakage problem in the data checking procedure. It remains open to design a secure POR scheme with both public verifiability and constant communication cost. To solve the above problems , we propose a novel preserving-private POR scheme with public verifiability and constant communication cost based on end-to-end aggregation authentication in this paper. To resist information leakage, we include zero-knowledge technique to hide the data in the integrity checking process. Our scheme is shown to be secure and efficient by security analysis and performance analysis. The security of our scheme is related to the Computational Diffie–Helleman Problem and Discrete logarithm problem. Finally, we also extend the POR scheme to support multi-file integrity checking and simulation results show that the verifier only needs less computational cost to achieve data integrity checking in our extended scheme.     

A security mechanism based on evolutionary game in fog computing

Fog computing is a distributed computing paradigm at the edge of the network and requires cooperation of users and sharing of resources. When users in fog computing open their resources, their devices are easily intercepted and attacked because they are accessed through wireless network and present an extensive geographical distribution. In this study, a credible third party was introduced to supervise the behavior of users and protect the security of user cooperation. A fog computing security mechanism based on human nervous system is proposed, and the strategy for a stable system evolution is calculated. The MATLAB simulation results show that the proposed mechanism can reduce the number of attack behaviors effectively and stimulate users to cooperate in application tasks positively.     

IEEE 802.11 Wireless LAN: Saturation Throughput Analysis with Seizing Effect Consideration

The IEEE 802.11 network technology is the emerging standard for wireless LANs and mobile networking. The fundamental access mechanism in the IEEE 802.11 MAC protocol is the Distributed Coordination Function. In this paper, we present an analytical method of estimating the saturation throughput of 802.11 wireless LAN in the assumption of ideal channel conditions. The proposed method generalizes the existing 802.11 LAN models and advances them in order to take the Seizing Effect into consideration. This real-life effect consists in the following: the station that has just completed successfully its transmission has a better chance of winning in the competition and therefore of seizing the channel than other LAN stations. The saturation throughput of 802.11 wireless LANs is investigated by the developed method. The obtained numerical results are validated by simulation and lead to the change of the existing idea of the optimal access strategy in the saturation conditions.     

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.     

Efficient public key encryption with revocable keyword search in cloud computing

Public key encryption with keyword search plays very important role in the outsourced data management. In most of public key encryption schemes with keyword search, the server can unlimitedly execute keyword search ability after obtaining a trapdoor information of a keyword. To restrict the ability of the server’s unlimited search, we propose a novel public key encryption with revocable keyword search by combining hash chain and anonymous multi-receiver encryption scheme in this paper. The scheme can not only achieve security property of the indistinguishability of ciphertexts against an adaptive chosen keywords attack, but also resist off-line keyword guess attack. By comparison with Yu et al.’s scheme, our scheme is more efficient in terms of computational cost and communication overhead for the whole system.     

Systematic identification of common functional modules related to heart failure with different etiologies

The development of heart failure (HF) is a complex process that can be initiated by multiple etiologies. Identifying common functional modules associated with HF is a challenging task. Here, we developed a systems method to identify these common functional modules by integrating multiple expression profiles, protein interactions from four species, gene function annotations, and text information. We identified 1439 consistently differentially expressed genes (CDEGs) across HF with different etiologies by applying three meta-analysis methods to multiple HF-related expression profiles. Using a weighted human interaction network constructed by combining interaction data from multiple species, we extracted 60 candidate CDEG modules. We further evaluated the functional relevance of each module by using expression, interaction network, functional annotations, and text information together. Finally, five functional modules with significant biological relevance were identified. We found that almost half of the genes in these modules are hubs in the weighted network, and that these modules can accurately classify HF patients from healthy subjects. We also identified many significantly enriched biological processes that contribute to the pathophysiology of HF, including two new ones, RNA splicing and vesicle-mediated protein transport. In summary, we proposed a novel framework to analyze common functional modules related to HF with different etiologies. Our findings provide important insights into the complex mechanism of HF. Further biological experimentations should be required to validate these novel biological processes.     

STACRP: a secure trusted auction oriented clustering based routing protocol for MANET

In mobile ad hoc network?(MANET) nodes have a tendency to drop others’ packet to conserve its own energy. If most of the nodes in a network start to behave in this way, either a portion of the network would be isolated or total network functionality would be hampered. This behavior is known as selfishness. Therefore, selfishness mitigation and enforcing cooperation between nodes is very important to increase the availability of nodes and overall throughput and to achieve the robustness of the network. Both credit and reputation based mechanisms are used to attract nodes to forward others’ packets. In light of this, we propose a game theoretic routing model, Secure Trusted Auction oriented Clustering based Routing Protocol (STACRP), to provide trusted framework for MANET. Two auction mechanisms procurement and Dutch are used to determine the forwarding cost-per-hop for intermediate nodes. Our model is lightweight in terms of computational and communication requirements, yet powerful in terms of flexibility in managing trust between nodes of heterogeneous deployments. It manages trust locally with minimal overhead in terms of extra messages. STACRP organizes the network into 1-hop disjoint clusters and elects the most qualified and trustworthy nodes as Clusterhead. The trust is quantified with carefully chosen parameters having deep impact on network functionality. The trust model is analyzed using Markov chain and is proven as continuous time Markov chain. The security analysis of the model is analyzed to guarantee that the proposed approach achieves a secure reliable routing solution for MANETs. The proposed model have been evaluated with a set of simulations that show STACRP detects selfish nodes and enforces cooperation between nodes and achieves better throughput and packet delivery ratio with lees routing overhead compare to AODV.     

Performance analysis of virtual clusters in personal communication networks

The objective of the paper is to analyze the performance of virtual cluster architectures in wireless networks. The key issues in wireless domain are flooding, connectivity, and power management. These issues arise during the path finding and maintenance between source and destination nodes. To overcome these issues three approaches are introduced in this paper namely; fusion virtual structure, link quality connected dominating set and cluster backbone approach. These approaches follow the distributed localized computations for virtual cluster constructions and focus on fundamental connectivity problems and are partially involved in power saving process of individual nodes. The proposed methods are analyzed in terms of backbone size, packet delivery ratio and normalized routing overhead and the results are witnessed by simulation.     

Epidemic Contact Tracing via Communication Traces

Traditional contact tracing relies on knowledge of the interpersonal network of physical interactions, where contagious outbreaks propagate. However, due to privacy constraints and noisy data assimilation, this network is generally difficult to reconstruct accurately. Communication traces obtained by mobile phones are known to be good proxies for the physical interaction network, and they may provide a valuable tool for contact tracing. Motivated by this assumption, we propose a model for contact tracing, where an infection is spreading in the physical interpersonal network, which can never be fully recovered; and contact tracing is occurring in a communication network which acts as a proxy for the first. We apply this dual model to a dataset covering 72 students over a 9 month period, for which both the physical interactions as well as the mobile communication traces are known. Our results suggest that a wide range of contact tracing strategies may significantly reduce the final size of the epidemic, by mainly affecting its peak of incidence. However, we find that for low overlap between the face-to-face and communication interaction network, contact tracing is only efficient at the beginning of the outbreak, due to rapidly increasing costs as the epidemic evolves. Overall, contact tracing via mobile phone communication traces may be a viable option to arrest contagious outbreaks.     

Evolutionarily conserved allosteric network in the Cys loop family of ligand-gated ion channels revealed by statistical covariance analyses

The Cys loop family of ligand-gated ion channels mediate fast synaptic transmission for communication between neurons. They are allosteric proteins, in which binding of a neurotransmitter to its binding site in the extracellular amino-terminal domain triggers structural changes in distant transmembrane domains to open a channel for ion flow. Although the locations of binding site and channel gating machinery are well defined, the structural basis of the activation pathway coupling binding and channel opening remains to be determined. In this paper, by analyzing amino acid covariance in a multiple sequence alignment, we have identified an energetically interconnected network in the Cys loop family of ligand-gated ion channels. Statistical coupling and correlated mutational analyses along with clustering revealed a highly coupled cluster. Mapping the positions in the cluster onto a three-dimensional structural model demonstrated that these highly coupled positions form an interconnected network linking experimentally identified binding domains through the coupling region to the gating machinery. In addition, these highly coupled positions are also condensed in the transmembrane domains, which are a recent focus for the sites of action of many allosteric modulators. Thus, our results revealed a genetically interconnected network that potentially plays an important role in the allosteric activation and modulation of the Cys loop family of ligand-gated ion channels.     

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.     

Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning

Compared with the traditional network tasks, the emerging Internet of Vehicles (IoV) technology has higher requirements for network bandwidth and delay. However, due to the limitation of computing resources and battery capacity of existing mobile devices, it is hard to meet the above requirements. How to complete task offloading and calculation with lower task delay and lower energy consumption is the most important issue. Aiming at the task offloading system of the IoV, this paper considers the situation of multiple MEC servers when modeling, and proposes a dynamic task offloading scheme based on deep reinforcement learning. It improves the traditional Q-Learning algorithm and combines deep learning with reinforcement learning to avoid dimensional disaster in the Q-Learning algorithm. Simulation results show that the proposed algorithm has better performance on delay, energy consumption, and total system overhead under the different number of tasks and wireless channel bandwidth.