A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

H-NAMe: specifying, implementing and testing a hidden-node avoidance mechanism for wireless sensor networks

The hidden-node problem has been shown to be a major source of Quality-of-Service (QoS) degradation in Wireless Sensor Networks (WSNs) due to factors such as the limited communication range of sensor nodes, link asymmetry and the characteristics of the physical environment. In wireless contention-based Medium Access Control protocols, if two nodes that are not visible to each other transmit to a third node that is visible to the formers, there will be a collision – usually called hidden-node or blind collision. This problem greatly affects network throughput, energy-efficiency and message transfer delays, which might be particularly dramatic in large-scale WSNs. This technical report tackles the hidden-node problem in WSNs and proposes HNAMe, a simple yet efficient distributed mechanism to overcome it. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes and then scales to multiple clusters via a cluster grouping strategy that guarantees no transmission interference between overlapping clusters. We also show that the H-NAMe mechanism can be easily applied to the IEEE 802.15.4/ZigBee protocols with only minor add-ons and ensuring backward compatibility with the standard specifications. We demonstrate the feasibility of H-NAMe via an experimental test-bed, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. We believe that the results in this technical report will be quite useful in efficiently enabling IEEE 802.15.4/ZigBee as a WSN protocol

H-NAMe: a hidden-node avoidance mechanism for wireless sensor networks

The hidden-node problem has been shown to be a major source of Quality-of-Service (QoS) degradation in Wireless Sensor Networks (WSNs) due to factors such as the limited communication range of sensor nodes, link asymmetry and the characteristics of the physical environment. In wireless contention-based Medium Access Control protocols, if two nodes that are not visible to each other transmit to a third node that is visible to the formers, there will be a collision – usually called hidden-node or blind collision. This problem greatly affects network throughput, energy-efficiency and message transfer delays, which might be particularly dramatic in large-scale WSNs. This paper tackles the hiddennode problem in WSNs and proposes H-NAMe, a simple yet efficient distributed mechanism to overcome it. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes and then scales to multiple clusters via a cluster grouping strategy that guarantees no transmission interference between overlapping clusters. We also show that the H-NAMe mechanism can be easily applied to the IEEE 802.15.4/ZigBee protocols with only minor add-ons and ensuring backward compatibility with the standard specifications. We demonstrate the feasibility of H-NAMe via an experimental test-bed, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. We believe that the results in this paper will be quite useful in efficiently enabling IEEE 802.15.4/ZigBee as a WSN protoco

Towards Flexible and Cognitive Production—Addressing the Production Challenges

Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Energy efficient data collection and dissemination protocols in self-organised wireless sensor networks

Wireless sensor networks (WSNs) are used for event detection and data collection in a plethora of environmental monitoring applications. However a critical factor limits the extension of WSNs into new application areas: energy constraints. This thesis develops self-organising energy efficient data collection and dissemination protocols in order to support WSNs in event detection and data collection and thus extend the use of sensor-based networks to many new application areas. Firstly, a Dual Prediction and Probabilistic Scheduler (DPPS) is developed. DPPS uses a Dual Prediction Scheme combining compression and load balancing techniques in order to manage sensor usage more efficiently. DPPS was tested and evaluated through computer simulations and empirical experiments. Results showed that DPPS reduces energy consumption in WSNs by up to 35% while simultaneously maintaining data quality and satisfying a user specified accuracy constraint. Secondly, an Adaptive Detection-driven Ad hoc Medium Access Control (ADAMAC) protocol is developed. ADAMAC limits the Data Forwarding Interruption problem which causes increased end-to-end delay and energy consumption in multi-hop sensor networks. ADAMAC uses early warning alarms to dynamically adapt the sensing intervals and communication periods of a sensor according to the likelihood of any new events occurring. Results demonstrated that compared to previous protocols such as SMAC, ADAMAC dramatically reduces end-to-end delay while still limiting energy consumption during data collection and dissemination. The protocols developed in this thesis, DPPS and ADAMAC, effectively alleviate the energy constraints associated with WSNs and will support the extension of sensorbased networks to many more application areas than had hitherto been readily possible

Building a more sustainable sensor network via protocol innovation

Traditionally, network protocols are designed based on the assumptions that network is powered by small batteries with scarce energy supply. However, emerging energy replenishment technologies such as ambient energy harvesting, wireless energy transferring, etc., provide alternatives to address the energy constraint problem but also introduce new challenges (e.g., energy heterogeneity). Been the core to achieve network sustainability, novel network protocols shall be designed to better exploit energy availabilities and tackle new challenges or issues exposed by emerging energy replenishment technologies. In this dissertation, we study how to build a more sustainable sensor network via network protocol innovation. Specifically, the study is conducted in four directions. First of all, we study how to improve energy utilization efficiency on individual sensor nodes as a foundation to improve the network sustainability. Secondly, we study how to prolong the network lifetime as a whole through dynamically and collaboratively tuning MAC layer operational parameters between neighboring nodes. Thirdly, we study the cross-layer design technique and propose a holistic routing and MAC protocol to further prolong the network lifetime. Fourthly, with given sensing coverage constraints, we jointly optimize the routing and sensing behaviors to further improve the network sustainability