Composite event coverage in wireless sensor networks with heterogeneous sensors

Peng

International Journal of Distributed Sensor Networks

et al. 2016

Wireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. In these applications, the first step is to detect whether the target is present or not. However, most of the existing work uses the "simple disk model" as signal model, which may not capture the sensing environment. In this work, we utilize a more realistic signal model to describe sensing process of sensors. On the other hand, the "majority rule" is widely used to make the final decision, which may not obtain the true judgment. To this end, we utilize a more realistic signal model and also use a probabilistic decision model to make the final decision. Moreover, we propose a probabilistic detection algorithm in which all sensors' local measurement values are fully used. This algorithm does not need any artificial threshold compared with traditional algorithms. It makes the most of spatiotemporal information to obtain the final decision. For the spatial perspective, sensors are distributed in different locations cooperating with each other. Meanwhile, for the temporal perspective, multiround subdecisions are fused. The effectiveness of the proposed method is validated by extensive simulation results, which show high detection probabilities and low false alarm probabilities.

“…(4) if 0 > 1 then (5) return round decision 0; (6) else (7) return round decision 1; determined that the target is present. Therefore, we define a testing function for global decision as…”

Section: Probabilistic Decision Methodmentioning

confidence: 99%

Extracting Target Detection Knowledge Based on Spatiotemporal Information in Wireless Sensor Networks

Peng

International Journal of Distributed Sensor Networks

et al. 2016

“…Wireless communication system is the important part for the Internet of Things (IoT) framework, which operates as the link for double directional communication for data gathering (sensing) and control message transmission . New promising tools provide the chance to utilize the complete potential of the IoT paradigm by expanding current wired connections with smart wireless constructions .…”

Section: State‐of‐the‐art Cache Replacement Algorithmsmentioning

confidence: 99%

An intelligent database proactive cache replacement policy for mobile communication system based on genetic programming

Darwish

El-Shnawy

2018

Int J Communication

In the mobile environment, the movement of the users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges in information access. Caching is considered one of the most important concepts to deal with these challenges.There are 2 general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date and cache replacement method chooses the cached element(s) that would be removed from the cache once the cache stays full. The aim of this work is to introduce a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, and validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were carried by NS2 simulator to assess the efficiency of the proposed method, and the outcomes are judged against existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches. KEYWORDScache invalidation, cache replacement, genetic programming, mobile database | INTRODUCTIONWith the rapid changes in technology, mobile communications are the essential part of our daily life. People are using the wireless network for making phone calls, accessing the Internet to download news, the stock market, etc. Different techniques for enhancing the capability and quality of wireless environments have become a hot research area. The development of mobile information systems has met some disadvantages. Mobile machines regularly have slight storage dimension and inadequate energy. Furthermore, mobile client users in wireless environments suffer from frequent disconnections from the server, and also calling the server for information is costly. For that reason, scholars have discovered caching approaches for valuable data managing in the mobile podium. 1-3 Still, a key difficulty is how to preserve the collected items in the cache up to date with the source data at the server.

“…To monitor the service behavior of facilities, various sensor networks have been deployed in facilities. Previous sensor-based works mainly focused on coverage [12], routing [13,14], data aggregation [15,16], and sensory data analytics [17]. However, moving people and vehicles outside facilities also need to be monitored and analyzed.…”

Section: Introductionmentioning

confidence: 99%

Influential spatial facility prediction over large scale cyber-physical vehicles in smart city

Feng

et al. 2016

J Wireless Com Network

Facilities are critical infrastructures in smart city. Influence of facilities is affected by large-scale moving objects such as people and vehicles. Calculating the influence of facilities is an important task of urban computing, which adopts sensing technology to obtain objects' movement patterns in urban space and applies this information to discover many hidden issues cities face today. In this paper, we propose a computationally efficient grid partition method to compute the influence of facilities in real time, under trajectories of large-scale cyber-physical vehicles. We next predict the influence changes of facilities over dynamic vehicles using trajectory-based Markov model. We conduct evaluation using a real-world dataset, including 1-month taxi trajectories with 27,000 taxis and 1000 facilities. Experimental results shows that our solution is more efficient and achieves an accuracy of 85 %.