Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Abstract

Abstract:Transferring emergent target tracking data to sinks is a major challenge in the Industrial Internet of Things (IIoT), because inefficient data transmission can cause significant personnel and property loss. For tracking a constantly moving mobile target, sensing data should be delivered to sinks continuously and quickly. Although there is some related research, the end to end tracking delay is still unsatisfying. In this paper, we propose a Fast and Efficient Data Forwarding (FEDF) scheme for tracking mobile targets in sensor networks to reduce tracking delay and maintain a long lifetime. Innovations of the FEDF scheme that differ from traditional scheme are as follows: firstly, we propose a scheme to transmit sensing data through a Quickly Reacted Routing (QRR) path which can reduce delay efficiently. Duty cycles of most nodes on a QRR path are set to 1, so that sleep delay of most nodes turn 0. In this way, end to end delay can be reduced significantly. Secondly, we propose a perfect method to build QRR path and optimize it, which can make QRR path work more efficiently. Target sensing data routing scheme in this paper belongs to a kind of trail-based routing scheme, so as the target moves, the routing path becomes increasingly long, reducing the working efficiency. We propose a QRR path optimization algorithm, in which the ratio of the routing path length to the optimal path is maintained at a smaller constant in the worst case. Thirdly, it has a long lifetime. In FEDF scheme duty cycles of nodes near sink in a QRR path are the same as that in traditional scheme, but duty cycles of nodes in an energy-rich area are 1. Therefore, not only is the rest energy of network fully made use of, but also the network lifetime stays relatively long. Finally, comprehensive performance analysis shows that the FEDF scheme can realize an optimal end to end delay and energy utilization at the same time, reduce end to end delay by 87.4%, improve network energy utilization by 2.65%, and ensure that network lifetime is not less than previous research.

An Aggregate Signature Based Trust Routing for Data Gathering in Sensor Networks

“…However, preservation of information and sensitive data is one of the biggest challenges in sensor networks [17][18][19]. Due to the high costs, the hardware of sensor nodes is relatively simple and is often unattended in hostile environment, hence prone to different kinds of attacks [20][21][22]. According to the statistics, there are over 30 kinds of attacks against the sensor network [21,23], including black attack, clone attack, selective forwarding attack, and false data injection attack [20][21][22][23].…”

“…Among them, some were conducted against a single type of attack behavior; for example, the literature [23] proposed the multibranch routing scheme for defense against selective forwarding attack. Some other researches were not conducted on the safety security strategy against a certain attack; for example, the traceback strategy proposed in literature [22] belongs to the defense attack strategies. In traceback strategy, the IDs of pass-through routing nodes are added to the data packet, so when an attack occurs, the attack path will be reconstructed, which helps the system obtain the location of attack source and take measures to clear the malicious node.…”

“…Due to the high costs, the hardware of sensor nodes is relatively simple and is often unattended in hostile environment, hence prone to different kinds of attacks [20][21][22]. According to the statistics, there are over 30 kinds of attacks against the sensor network [21,23], including black attack, clone attack, selective forwarding attack, and false data injection attack [20][21][22][23]. It is necessary to provide solid as well as evident solutions as countermeasures against threats in sensor networks; otherwise the application of sensor networks will be hindered [20][21][22][23].…”

“…According to the statistics, there are over 30 kinds of attacks against the sensor network [21,23], including black attack, clone attack, selective forwarding attack, and false data injection attack [20][21][22][23]. It is necessary to provide solid as well as evident solutions as countermeasures against threats in sensor networks; otherwise the application of sensor networks will be hindered [20][21][22][23]. For example, black hole attack [23] is such an attack behavior which can drop all the data transferred through the black holes, and the selective forwarding attack (SFA) [20,24] can intelligently drop some packets to damage network which is difficult to detect and defense such attack [23].…”

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

“…Internet of Things (IoT) [1][2][3][4][5] and cloud computing [6][7][8][9][10][11][12][13] take advantage of the ubiquity of smart sensor-equipped devices such as smartphones, iPad, and vehicle sensor devices All of these are to collect information with low cost and provide a new paradigm for solving the complex data sensing based applications from the significant demands of critical infrastructure such as surveillance systems [14][15][16], remote patient care systems in healthcare, intelligent traffic management, and automated vehicles in transportation environmental [17][18][19] and weather monitoring systems. In such applications, due to the need to collect a wide range and large amount of data even in a long-term continuous period, the cost of traditional method deploying sensing devices or specialized employee to collect the data is so high limiting the development of applications [20,21].…”