Highly Resilient Key Distribution Strategy for Multi-level Heterogeneous Sensor Networks by Using Deployment Knowledge

The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited resources, for instance, low battery life and low computational power, the key distribution scheme must be designed in an efficient manner. Recently many studies added a few high-level nodes into the network, called the heterogeneous sensor network (HSN). Most of these studies considered an application for two-level HSN instead of multi-level one. In this paper, we propose some definitions for multi-level HSN, and design a novel key management strategy based on the polynomial hash tree (PHT) method by using deployment knowledge. Our proposed strategy has lower computation and communication overheads but higher connectivity and resilience.

Novel routing protocol for heterogeneous wireless sensor networks

An improved LEACH for heterogeneous wireless sensor networks is proposed. Nodes are distributed in a sensing area that is divided into a number of same equilateral hexagons. Heterogeneous nodes act as the cluster heads and ordinary nodes act as those cluster sensors in all clusters. The structure of WSNs is a two-layer structure. The upper layer consists of all cluster heads and the lower layer consists of all ordinary sensors managed by their corresponding cluster heads. The cluster heads and the ordinary sensors establish their pairwise keys respectively through utilizing different methods. The arithmetic balances energy expense among all kinds of nodes, saves the node energy, and prolongs the life of wireless sensor networks. Additionally, Analysis demonstrates that the security of wireless sensor networks has been improved obviously even with some heterogeneous nodes.

Optimal Control of Heterogeneous-Susceptible-Exposed-Infectious-Recovered-Susceptible Malware Propagation Model in Heterogeneous Degree-Based Wireless Sensor Networks

Heterogeneous wireless sensor networks(HWSNs)are vulnerable to malware propagation,because of their low configuration and weak defense mechanism.Therefore,an optimality system for HWSNs is developed to suppress malware propagation in this paper.Firstly,a heterogeneous-susceptible-exposed-infectious-recovered-susceptible(HSEIRS)model is proposed to describe the state dynamics of heterogeneous sensor nodes(HSNs)in HWSNs.Secondly,the existence of an optimal control problem with installing antivirus on HSNs to minimize the sum of the cumulative infection probabilities of HWSNs at a low cost based on the HSEIRS model is proved,and then an optimal control strategy for the problem is derived by the optimal control theory.Thirdly,the optimal control strategy based on the HSEIRS model is transformed into corresponding Hamiltonian by the Pontryagin’s minimum principle,and the corresponding optimality system is derived.Finally,the effectiveness of the optimality system is validated by the experimental simulations,and the results show that the infectious HSNs will fall to an extremely low level at a low cost.

Fuzzy Based BEENISH Protocol for Wireless Sensor Network

The main parameter to be considered in the wireless sensor network is the amount of energy that is available in each sensor node. The lifetime of the sensor node (SN) depends on it. As the SNs are deployed in remote locations, if the entire energy is consumed, it would be very difficult to replace or recharge the energy source immediately. Hence the energy consumed by each node is very important. If individual SNs send information directly to the base station (BS), then the availability of energy in such SN decreases very fast. This will lead to reduction in the life time of the SN. Instead, the SNs can send the data to the cluster head (CH), then the CH consolidates the received data. The CH sends it to the BS periodically. In this way, utilizing CH for sending the information to the BS increases the lifetime of the SN. The cluster head selection is very crucial in such networks. This paper proposes a novel fuzzy based BEENSIH protocol for CH selection.

Reliable Data Storage in Heterogeneous Wireless Sensor Networks by Jointly Optimizing Routing and Storage Node Deployment

In the era of big data,sensor networks have been pervasively deployed,producing a large amount of data for various applications.However,because sensor networks are usually placed in hostile environments,managing the huge volume of data is a very challenging issue.In this study,we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques.To minimize data delivery and data storage costs,we design an algorithm to jointly optimize data routing and storage node deployment.The problem can be formulated as a binary nonlinear combinatorial optimization problem,and due to its NP-hardness,designing approximation algorithms is highly nontrivial.By leveraging the Markov approximation framework,we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy.We also perform extensive simulations to verify the efficacy of our algorithm.

Distributed state estimation for heterogeneous mobile sensor networks with stochastic observation loss

The problem of distributed fusion and random observation loss for mobile sensor networks is investigated herein.In view of the fact that the measured values,sampling frequency and noise of various sensors are different,the observation model of a heterogeneous network is constructed.A binary random variable is introduced to describe the drop of observation component and the topology switching problem caused by complete observation loss is also considered.A cubature information filtering algorithm is adopted to design local filters for each observer to suppress the negative effects of measurement noise.To derive a consistent and accurate estimation result,a novel weighted average consensus-based filtering approach is put forward.For the sensor that suffers from observation loss,its local prediction information vector is fused with the information contribution vectors of the neighbors to obtain the local estimation.Then the consensus weight matrix is designed for consensus-based distributed collaborative information fusion.The boundness of the estimation errors is proved by employing the stochastic stability theory.In the end,two numerical examples are offered to assert the validity of the presented method.

Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.

Low-latency Data Gathering with Reliability Guaranteeing in Heterogeneous Wireless Sensor Networks

In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks(HWSNs),the problem of multi-channel-based data gathering with minimum latency(MCDGML),which associates with construction of data gathering trees,channel allocation,power assignment of nodes and link scheduling,is formulated as an optimization problem in this paper.Then,the optimization problem is proved to be NP-hard.To make the problem tractable,firstly,a multi-channel-based low-latency(MCLL)algorithm that constructs data gathering trees is proposed by optimizing the topology of nodes.Secondly,a maximum links scheduling(MLS)algorithm is proposed to further reduce the latency of data gathering,which ensures that the signal to interference plus noise ratio(SINR)of all scheduled links is not less than a certain threshold to guarantee the reliability of links.In addition,considering the interruption problem of data gathering caused by dead nodes or failed links,a robust mechanism is proposed by selecting certain assistant nodes based on the defined one-hop weight.A number of simulation results show that our algorithms can achieve a lower data gathering latency than some comparable data gathering algorithms while guaranteeing the reliability of links,and a higher packet arrival rate at the sink node can be achieved when the proposed algorithms are performed with the robust mechanism.