Chi Lin

Energy efficient ant colony algorithms for data aggregation in wireless sensor networks

In energy-constrained wireless sensor networks, energy efficiency is critical for prolonging the network lifetime. A family of ant colony algorithms called DAACA for data aggregation are proposed in this paper. DAACA consists of three phases: initialization, packets transmissions and operations on pheromones. In the transmission phase, each node estimates the remaining energy and the amount of pheromones of neighbor nodes to compute the probabilities for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustments are performed, which take the advantages of both global and local merits for evaporating or depositing pheromones. Four different pheromones adjustment strategies which constitute DAACA family are designed to prolong the network lifetime. Experimental results indicate that, compared with other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last, the features of DAACA are analyzed.

Dynamical Jumping Real-Time Fault-Tolerant Routing Protocol for Wireless Sensor Networks

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.

Clustering and splitting charging algorithms for large scaled wireless rechargeable sensor networks

Merging and clustering charging algorithms named HCCA and HCCA-TS are proposed for WRSN.HCCA combines K-means clustering and hierarchical clustering for enhancing charging efficiency.HCCA-TS optimizes the performance of HCCA from a task splitting view. As the interdiscipline of wireless communication and control engineering, the periodical charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. However, existing techniques for periodical charging neglect to focus on the location relationship and topological feature, leading to large charging times and long traveling time. In this paper, we develop a hybrid clustering charging algorithm (HCCA), which firstly constructs a network backbone based on a minimum connected dominating set built from the given network. Next, a hierarchical clustering algorithm which takes advantage of location relationship, is proposed to group nodes into clusters. Afterward, a K-means clustering algorithm is implemented to calculate the energy core set for realizing energy awareness. To further optimize the performance of HCCA, HCCA-TS is proposed to transform the energy charging process into a task splitting model. Tasks generated from HCCA are split into small tasks, which aim at reducing the charging time to enhance the charging efficiency. At last, simulations are carried out to demonstrate the merit of the schemes. Simulation results indicate that HCCA can enhance the performance in terms of reducing charging times, journey time and average charging time simultaneously. Moreover, HCCA-TS can further improve the performance of HCCA.

Differential Privacy Preserving in Big Data Analytics for Connected Health

In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.

TADP: Enabling temporal and distantial priority scheduling for on-demand charging architecture in wireless rechargeable sensor Networks

Abstract Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attentions from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than systems harvesting dynamic energy from the surrounding environment. Wireless power transfer technique provides a new alternative for solving the limited power capacity problem for so many popular mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of the unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies for on-demand WRSNs architecture achieve rather low charging request throughput or successful rate, posing as a major bottleneck for further improvements. In this paper, we propose a T empor A l & D istantial P riority charging scheduling algorithm (TADP), which takes both the distance between nodes and the mobile charger and the arrival time of charging requests into consideration, and quantizes these two factors step by step. TADP forms a mixed priority queue which directs mobile charger to replenish the energy for nodes. At last extensive simulations are conducted to demonstrate the advantages of TADP. Simulation results reveal that TADP can achieve better scheduling performance in guaranteeing the scheduling success of the high-priority tasks and improving stability of the system.

Enhancing the attacking efficiency of the node capture attack in WSN: a matrix approach

In the node capture attack, the adversary intelligently captures nodes and extracts the cryptographic keys from their memories to destroy the security, reliability and confidentiality of the wireless sensor networks. However, it suffers from low attacking efficiency and high resource expenditure. In this paper, we approach this attack from an adversarial view and develop a matrix-based method to model the process of the node capture attack. We establish a matrix to indicate the compromising relationship between the nodes and the paths. We propose a Matrix-based node capture attack Algorithm (MA in short), which can maximize the destructiveness while consuming the minimum resource expenditure. We conduct several experiments to show the performance of MA. Experimental results manifest that MA can reduce the attacking round, shorten the execution time, enhance the attacking efficiency and conserve the energy cost.

Enhancing Efficiency of Node Compromise Attacks in Vehicular Ad-hoc Networks Using Connected Dominating Set

In the node compromise attack, the adversary physically captures nodes and extracts the cryptographic keys from the memories, which destroys the security, reliability and confidentiality of the networks. Due to the dynamical network topology, designing an efficient node compromise attack algorithm is challenging, because it is difficult to model the attack or to enhance the attacking efficiency. In this paper, a general algorithm for modeling the node compromise attack in VANET is proposed, which promotes the attacking efficiency by destroying the network backbone. The backbone is constructed using the connected dominating set of the network, which has relevant to the intermeeting time between the vehicles. Then two attacking algorithms are proposed based on the general model, which destroy the network in a centralized and distributed version while maximizing the destructiveness. Simulations are conducted to show the advantages of our scheme. Simulation results reveal that our scheme enhances the attacking efficiency in different mobility models and different applications, which is suitable for modeling the node compromise attack in VANET. At last, discussions are presented to the illustrate the influences of the characteristics to the attacking efficiency with respect to vehicle speed, communication range and key sharing probability.

GTCharge: A game theoretical collaborative charging scheme for wireless rechargeable sensor networks

Abstract Collaborative charging schemes are indeed helpful for energy replenishment. However, classic and traditional collaborative charging schemes are still suffering from a series of severe problems, which are almost neglected. The lack of homogeneity and dynamic charging decisions on collaborative charging schemes in Wireless Rechargeable Sensor Networks (WRSN) deteriorate the charging efficiency. To enhance charging performance, especially in terms of charging efficiency, in this paper, a game theoretical collaborative charging scheme, namely GTCharge is devised. The charging process is converted into a collaborative game taken between wireless charging vehicles (WCVs). We investigate the functionalities of contribution degree, charging priority and profits. Then GTCharge is demonstrated in detail, in which each WCV seeks for the maximum profit when fulfilling charging tasks. The conditions including all WCVs’ charging strategies are proven to reach a Nash Equilibrium point. Finally, extensive simulations are conducted to show the advantages of the proposed scheme. Simulation results demonstrate the merits of the proposed scheme in terms of charging efficiency.

TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks

The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a temporal-spatial charging scheduling algorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.

DWDP: A Double Warning Thresholds with Double Preemptive Scheduling Scheme for Wireless Rechargeable Sensor Networks

Wireless power transfer technique provides new alternatives for solving the limited power capacity problem for so many popular mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of the unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies for on-demand WRSNs architecture achieve rather low charging request throughput or successful rate, posing as a major bottleneck for further improvement. In this paper, we propose a Double Warning Thresholds with Double Preemption (DWDP) charging scheme, in which double warning thresholds are used when residual energy levels of sensor nodes fall below certain thresholds. By introducing specific comparison rules, warning thresholds can be used to adjust charging priorities of different sensors, warn the upcoming recharge deadlines, as well as support preemptive scheduling. We perform extensive simulations to manifest the advantages of DWDP. Simulation results reveal that DWDP can achieve better scheduling performance, in guaranteeing the scheduling success of the high-priority task and improving stability of the system.