Chuan Zhu

A Tree-Cluster-Based Data-Gathering Algorithm for Industrial WSNs With a Mobile Sink

Wireless sensor networks (WSNs) have been widely applied in various industrial applications, which involve collecting a massive amount of heterogeneous sensory data. However, most of the data-gathering strategies for WSNs cannot avoid the hotspot problem in local or whole deployment area. Hotspot problem affects the network connectivity and decreases the network lifetime. Hence, we propose a tree-cluster-based data-gathering algorithm (TCBDGA) for WSNs with a mobile sink. A novel weight-based tree-construction method is introduced. The root nodes of the constructed trees are defined as rendezvous points (RPs). Additionally, some special nodes called subrendezvous points (SRPs) are selected according to their traffic load and hops to root nodes. RPs and SRPs are viewed as stop points of the mobile sink for data collection, and can be reselected after a certain period. The simulation and comparison with other algorithms show that our TCBDGA can significantly balance the load of the whole network, reduce the energy consumption, alleviate the hotspot problem, and prolong the network lifetime.

Secure Localization in Wireless Sensor Networks: A Survey (Invited Paper)

Secure localization of unknown nodes in a Wireless Sensor Network (WSN) is an important research subject. When WSNs are deployed in hostile environments, many attacks happen, e.g., wormhole, sinkhole and sybil attacks. Two issues about unknown nodes’ secure localization need to be considered. First, the attackers may disguise as or attack the unknown and anchor nodes to interfere with localization process. Second, the attackers may forge, modify or replay localization information to make the estimated positions incorrect. Currently, researchers have proposed many techniques, e.g., SeRLoc, HiRLoc and ROPE, to solve the two issues. In this paper we describe the common attacks against localization, and survey research state of secure localization.

A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink

Mobile sink is widely used for data collection in wireless sensor networks. It can avoid ‘hot spot’ problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios.

A honeycomb structure based data gathering scheme with a mobile sink for wireless sensor networks

With the widespread use of wireless sensor networks, more and more applications require energy efficient and low packet loss rate data collection methods. Recently, the concept of ‘mobile’ is introduced in various mechanisms to meet the needs of this kind. In this paper, a honeycomb structure based data gathering scheme, HSDG, is proposed for wireless sensor networks with a mobile sink. By partitioning the network into a honeycomb structure and giving each partition a direction value, every sensor node can obtain the latest location of the mobile sink dynamically with a small amount of broadcasting overhead. HSDG uncouples the moving strategy of mobile sink from the data forwarding mechanism, and three subscheme HSDG_RM, HSDG_DGM, and HSDG_EGM are proposed. Our schemes are investigated from average energy consumption, maintenance cost, packet loss rate and the number of packets collected. Compared with BTDG and ALURP, HSDG_DGM is the most energy efficient with a low packet loss rate.

LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.

A Location Prediction Based Data Gathering Protocol for Wireless Sensor Networks Using a Mobile Sink

Traditional data gathering protocols in wireless sensor networks are mainly based on static sink, and data are routed in a multi-hop manner towards sink. In this paper, we proposed a location predictable data gathering protocol with a mobile sink. A sink’s location prediction principle based on loose time synchronization is introduced. By calculating the mobile sink location information, every source node in the network is able to route data packets timely to the mobile sink through multi-hop relay. This study also suggests a dwelling time dynamic adjustment method, which takes the situation that different areas may generate different amount of data into account, resulting in a balanced energy consumption among nodes. Simulation results show that our data gathering protocol enables data routing with less data transmitting time delay and balance energy consumption among nodes.

Two-Hop Geographic Multipath Routing in Duty-cycled Wireless Sensor Networks

As an extension of our previous designed Two-Phase geographic Greedy Forwarding (TPGF) routing algorithm in wireless sensor networks (WSNs), this paper proposes a new 2-hop geographic greedy forwarding algorithm called TPGFPlus, which uses 2-hop neighborhood information for geographic routing. In our TPGFPlus, a forwarding node selects its next-hop node which is closest to the based station among all its 1-hop and 2-hop neighbor nodes. Moreover, to prolong network lifetime, not all the nodes are awake for working in our work since the EC-CKN algorithm is applied to make the network be duty-cycled. We evaluate the performance of our algorithm versus running existing TPGF algorithm on the same duty-cycled WSNs. Simulations show that our proposed algorithm outperforms previous work TPGF on finding more average paths and shorter average length of paths, yet without causing additional energy consumption.

A Survey and Taxonomy of Energy Efficiency Relevant Surveys in Cloud-Related Environments

The problem of energy consumption in the cloud related environments has attracted a great deal of attention in research and industry communities. In the past ten years, a large number of studies have been done on energy efficiency algorithms, methods, strategies, or techniques. Over the past five years, many scholars have conducted surveys on those energy efficiency strategies from different perspectives. In this paper, we conduct a survey and build the taxonomy on current existing energy efficiency relevant surveys, that is, a survey on surveys of energy efficiency. We classify current existing surveys into five categories, including surveys on the energy efficiency of the whole cloud related systems, surveys on energy efficiency of the certain level or component of the cloud, surveys on all of energy efficient strategies, surveys on a certain energy efficiency techniques, and other energy efficiency related surveys. The survey and taxonomy on the energy efficient relevant surveys conducted in this paper provide comprehensive knowledge on the current level of this research filed. Furthermore, observations are exhibited by the statistic data graphs exacted from the investigated surveys. The observations hidden in those surveys on energy efficiency will make it clear the future research directions in the energy related research field. Future research directions are listed in the final section.

Block-based adaptive compressed sensing with feedback for DCVS

In this paper, we propose a rate adaptive compressed sensing for block-based DCVS framework, in which the sampling rate is estimated at the decoder side and can be sent back to the encoder via the return channel. The assignment of measurements rate (MR) is depending on the variance of the residual measurements between SI and the reconstructed CS frame. By comparing with the encoder-based scheme, the proposed scheme can achieve a comparable result with more stable and smaller transmission rate, which can briefly increase the transform efficiency as well as reduce the band width.

Performance evaluation of DV-hop localization algorithm with mobility models for Mobile Wireless Sensor Networks

In Mobile Wireless Sensor Networks (MWSNs), location information of nodes is the basis of many applications. Compared with that in a static network, the localization issue in a mobile network is more difficult due to uncertainty of node positions, thus much more challenges are brought to MWSNs. In this paper, we propose three localization models to evaluate performances of DV-hop localization algorithm in MWSNs. We compare the performance of three localization models, DV-hop+RWP, DV-hop+RD and DV-hop+RPGM, with that of DV-hop. Simulation results show that, DV-hop+RD has higher localization accuracy and lower energy consumption than others. However, when the number of nodes increases, more unknown nodes in DV-hop+RWP can be localized than that in DV-hop+RD. The localization success and localization accuracy of DV-hop+RPGM, which is similar to that of DV-hop, is lower than DV-hop+RWP.