Energy Balanced Redeployment Algorithm for Heterogeneous Wireless Sensor Networks

In wireless sensor networks, the interference around the application environment may cause the actual distance between any pair of nodes to fail to be measured accurately. Enclosure graph (EG) model uses this distance between nodes as its weight to construct the topology, which does not fully consider the interference. Consequently it will lead to a large amount of energy consumption induced by the application environment. Even it shortens the survival time. According to the feature of network energy inequality in a wireless sensor network and the defect of EG, we first introduce the adjustable factor of node degree, establish a model of communication metric and a model for the node actual survival time. Then according to the demand of network energy equalization and maximum network lifetime, we quantitatively analyze the network node degree, and achieve its regular pattern. In accordance with this regular pattern and sufficient conditions of function extremum, the maximum node energy consumption and the maximum node actual survival time are deduced. And the corresponding optimal node degree is achieved. Finally, according to the above model, in this paper we propose an energy balance and robustness adjustable topology control algorithm for wireless sensor networks. Theoretical analyses show that this algorithm can guarantee that the network is connected and the link of the network is bi-directionally connected. Experiments show that the network takes advantage of this optimal node degree to obtain the high robustness, thus guaranteeing that the information can be transferred unfailingly. This algorithm can effectively balance the node energy, improve the node survival time, enhance the network robustness, and prolong the network's lifetime.

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.

This paper proposes a novel deployment algorithm for wireless sensor networks namely VFIPO based on individual particle optimization (IPO) incorporated with virtual force (VF) algorithm, which overcomes the shortcomings of applying the VF algorithm. Simulation results verify that mobile nodes deployment with VFIPO outperforms the previous deployment algorithms such as VF, PSO, VFPSO and IPO based approaches with respect to effective coverage area and computation time.

In real-world applications, wireless sensor networks often consist of a large number of sensor nodes with constraint battery resources. How to reduce the power consumption of sensor nodes and maximize the network life, becomes the most important goal of topology control schemes in wireless sensor networks. During the operation of networks, sensor nodes may spend different levels of energy, and result in the uneven distribution of residual energy of sensor nodes. In order to extend the network life, it is essential to adjust the network burden of sensor nodes dynamically, so as to achieve energy balance among nodes under the consideration of different energy levels at nodes. In this paper, we introduce the game theory and the concept of game potential. By synthetically considering the factors of the residual energy and transmission power of nodes, a potential game based mathematical model of topology control is constructed. We prove the existence of Nash equilibrium. Through designing a payoff function, which takes into account both network connectivity and energy balance of nodes, the connectivity of sensor networks can be maintained while the power of sensor nodes is reduced. By increasing the average value of residual energy of neighbors, it enables to select nodes with more energy that reserves in neighborhood as neighbors, to improve the energy balance among nodes. Based on that, a distributed energy-balanced topology control algorithm (DEBA) is proposed. Theoretical analysis proves that the algorithm can maintain network connectivity. Compared with other existing game theory based algorithms DIA and MLPT, the topologies formed by the proposed algorithm have fewer bottleneck nodes which feature heavy traffic load and low residual energy, and smaller variance of node residual energy, thus achieving a longer life.

Wireless sensor networks (WSNs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSNs. In particular, quality-of-service (QoS) management in WSNs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a logic for QoS management (LFQMS) scheme for WSNs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSNs. It utilizes a logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the sensor. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The (LFQMS) has the advantages of generality, scalability, and simplicity. Simulation results show that the (LFQMS) can provide WSNs with QoS support.

Most of the proposed algorithms focus on static networks of sensors with either static or mobile anchors, in which the Monte Carlo localization algorithm is a typical one for localizing nodes in a mobile wireless sensor network. But the radio range being all different or inconstant in this algorithm leads to reduce the accuracy of localization and the efficiency of the algorithm itself. In this article, we propose the novel rang-based stochastic Monte Carlo localization algorithm for wireless sensor networks specifically designed with mobility to improve the accuracy of localization by dealing with the different radio ranges of sensors, and being bound to the narrow sampling area. Our simulation experimental results show that the rang-based stochastic Monte Carlo localization algorithm has improved the accuracy and stability of the estimated locations.

Connected dominated subsets (CDS) of nodes in wireless sensor network are always selected to form a virtual backbone. Construction of r-hop 2-connected dominating sets can reduce the size of CDS and the total amount of information to be transmitted. The 2-connected properties can provide higher fault tolerance and robustness as well. In this paper an approximation r-hop 2-connected dominating set algorithm for wireless sensor and ad-hoc networks is presented. The main strategy is partition to three phases: in the first phase, an r-hop independent dominating set is established; in the second phase, the r-hop dominating set is interconnected successively to form a connected subgraph; in the third phase, more vertices are selected and added into dominating set to make it 2-connected. Extensive simulations according to the algorithm steps have been conducted. Performance with detailed analysis has been shown at last.

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

Denial of Service (DoS) attacks at the routing levels were earlier security's primary focus. Resource depletion attacks were formerly exclusively thought of as routing issues, but they have lately been included in a new category as “vampire attacks.” Every protocol under consideration is vulnerable to vampire attacks, which are destructive, difficult to identify, and simple to execute, with only one rogue insider sending only protocol-compliant communications. To solve these problems, Vampire attack detection by applying a Support Vector Machine (SVM) algorithm in a wireless sensor network (VSVM) is proposed. This mechanism uses the SVM algorithm to separate the vampire nodes ideally. The SVM algorithm isolates the vampire nodes based on sensor node characteristics like node energy, node broadcast count, and node packet received ratio (PRR). Simulation results reveal that the VSVM mechanism enhances the vampire attack detection ratio and throughput. Moreover, it minimizes the delay and routing overhead in the wireless sensor network.

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.

Congestion in Wireless Sensor Networks has negative impact on the Quality of Service. Congestion effects the performance metrics, namely throughput and per-packet energy consumption, network lifetime and packet delivery ratio. Reducing congestion allows better utilization of the network resources and thus enhances the Quality of Service metrics of the network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks reduces congestion by considering one hop neighbor routing in the network. This paper proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for congestion control in wireless sensor networks based on two hop neighbor information. On detection of congestion, the algorithm forwards data packets around the congestion areas by spreading the excessive packets through multiple paths. The path with light load or under loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the algorithm is to build path to the destination using two independent potential fields depth and queue length. Queue length field solves the traffic-aware problem. Depth field creates a backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential field to make dynamic decision for data forwarding. Network Simulator used for simulating the algorithm is NS2. The proposed algorithm performs better.

Aiming at the problems of low coverage of all fixed sensor nodes in wireless sensor networks and the high cost of deployment of mobile sensor nodes, a hybrid sensor network deployment strategy based on virtual force is proposed, and then the cluster node can find out the hot point (center point of the grid) which is not covered by the sensor node. Then the task is assigned to the mobile sensor nodes by the Hungarian algorithm. Each mobile node based on virtual force algorithm autonomously moves to the uncovered grid center or near the grid center. Simulation results show that the deployment strategy can effectively improve the coverage of the network, and it is superior to the virtual force algorithm deployment strategy based on all mobile sensor nodes in terms of coverage rate and moving distance.

In general, wireless sensor network works by a small battery-powered, or limited energy. Once the wireless sensor network is deployed, the energy of small sensor nodes can not be replaced. So, to improve energy efficiency and extend the survival time of the whole network is a crucial issue. This paper presents a new type of energy balancing algorithm for wireless sensor networks. The BCDCP-M algorithm draws on the main idea of the BCDCP routing protocol. When the base station divides the network, the new algorithm makes the number of cluster heads equal to the optimal number of cluster heads as far as possible. On cluster head election, not only the average energy of the sensor network, but also the remaining energy of the individual node must be taken into account. In data transmission, we use the multi-hop method to select the optimal path. The simulation results show that the survival time of the network in the new BCDCP-M algorithm is 19% longer than BCDCP.

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.