Szymon Fedor

On the Problem of Energy Efficiency of Multi-Hop vs One-Hop Routing in Wireless Sensor Networks

The hop distance strategy in wireless sensor networks (WSNs) has a major impact on energy consumption of each sensor mote. Long-hop routing minimizes reception cost. However, a substantial power demand is incurred for long distance transmission. Since the transceiver is the major source of power consumption in the node, optimizing the routing for hop length can extend significantly the lifetime of the network. This paper explores when multi-hop routing is more energy efficient than direct transmission to the sink and the conditions for which the two-hop strategy is optimal. Experimental evidence is provided in to support of these conclusions. The tests showed that the superiority of the multi-hop scheme depends on the source-sink distance and reception cost. They also demonstrated that the two- hop strategy is most energy efficient when the relay is at the midpoint of the total transmission radius. Our results may be used in existing routing protocols to select optimal relays or to determine whether it is better to send packets directly to the base station or through intermediate nodes.

Magneto approach to QoS monitoring

Quality of Service (QoS) monitoring of end-user services is an integral and indispensable part of service management. However in large, heterogeneous and complex networks where there are many services, many types of end-user devices, and huge numbers of subscribers, it is not trivial to monitor QoS and estimate the status of Service Level Agreements (SLAs). Furthermore, the overwhelming majority of end-terminals do not provide precise information about QoS which aggravates the difficulty of keeping track of SLAs. In this paper, we describe a solution that combines a number of techniques in a novel and unique way to overcome the complexity and difficulty of QoS monitoring. Our solution uses a model driven approach to service modeling, data mining techniques on small sample sets of terminal QoS reports (from “smarter” end-user devices), and network level key performance indicators (N-KPIs) from probes to address this problem. Service modeling techniques empowered with a modeling engine and a purpose-built language hide the complexity of SLA status monitoring. The data mining technique uses its own engine and learnt data models to estimate QoS values based on N-KPIs, and feeds the estimated values to the modeling engine to calculate SLAs. We describe our solution, the prototype and experimental results in the paper.

Cross-layer routing and time synchronisation in wireless sensor networks

A common time reference across nodes is required in most Wireless Sensor Networks (WSNs) applications. Also many routing protocols require that nodes communicate according to some predefined schedule for reasons of energy efficiency. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology, may lead to a failure of the synchronisation protocol. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. We present two novel cross-layer routing algorithms, CLEAR and RISS. We implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence, it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs.

A visual programming framework for wireless sensor networks in smart home applications

Most of the currently deployed integrated home management products require an experienced technician to install and configure the system. In this paper, we build upon the Internet of Things (IoT) paradigm, with the aim of delivering networked solutions that enable multi-node wireless sensor networks (WSNs) to connect to the Internet in a secure, simple and efficient way. We also describe the design and implementation of a smart-home management system. The system is composed of a lightweight tool with an intuitive user interface for commissioning of IP-enabled WSNs. The solution includes a visual programming interface with a common framework for discovering smart home services on the WSN, and a code analysis and translation engine to generate Python code. This engine analyses the application rules defined with the graphical user interface and translates them into distributed application scripts. The system also includes modules to plan the optimization of the deployment, and deploy and start the generated code. In this paper we present a prototype of the system, with the visual programming solution and code generation module.

Synchronization Service Integrated into Routing Layer in Wireless Sensor Networks

The time synchronization problem needs to be considered in a distributed system. In Wireless Sensor Networks (WSNs) this issue must be solved with limited computational, communication and energy resources. Many synchronization protocols exist for WSNs. However, in most cases these protocols are independent entities with specific packets, communication scheme and network hierarchy. This solution is not energy efficient. Because it is very rare for synchronization not to be necessary in WSNs, we advocate integrating the synchronization service into the routing layer. We have implemented this approach in a new synchronization protocol called Routing Integrated Synchronization Service (RISS). Our tests show that RISS is very time and energy efficient and also is characterized by a small overhead. We have compared its performance experimentally to that of the FTSP synchronization protocol and it has proved to offer better time precision than the latter protocol.

A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network †

Contiki’s Cooja is a very popular wireless sensor network (WSN) simulator, but it lacks support for modelling sensing coverage, focusing instead on network connectivity and protocol performance. However, in practice, it is the ability of a sensor network to provide a satisfactory level of coverage that defines its ultimate utility for end-users. We introduce WSN-Maintain, a Cooja-based tool for coverage and network lifetime evaluation in an in-building WSN. To extend the network lifetime, but still maintain the required quality of coverage, the tool finds coverage redundant nodes, puts them to sleep and automatically turns them on when active nodes fail and coverage quality decreases. WSN-Maintain together with Cooja allow us to evaluate different approaches to maintain coverage. As use cases to the tool, we implement two redundant node algorithms: greedy-maintain, a centralised algorithm, and local-maintain, a localised algorithm to configure the initial network and to turn on redundant nodes. Using data from five real deployments, we show that our tool with simple redundant node algorithms and reading correlation can improve energy efficiency by putting more nodes to sleep.

A Cooja-based tool for maintaining sensor network coverage requirements in a building

Contikis Cooja is a very popular Wireless Sensor Network (WSN) simulator, but it lacks support for modelling sensing coverage. We introduce WSN-Maintain, a Cooja-based tool for maintaining coverage requirements in an in-building WSN. To analyse the coverage of a building, WSN-Maintain takes as input the floorplan of the building, the coverage requirement of each region and the locations of sensor nodes. We take account of the heterogeneity of device specifications in terms of communication capability and sensing coverage. WSN-Maintain is run in parallel with the collect-view tool of Contiki, which was integrated into the Cooja simulator. We show that WSN-Maintain is able to automatically turn on redundant nodes to maintain the coverage requirement when active nodes fail and report failures that require physical maintenance. This tool allows us to evaluate different approaches to maintain coverage, including deferring physical maintenance to reduce operational costs.

Sensor Networks’ Integration

Sensor networks and applications thereof have been intensively researched in the past decade and a variety of systems have been meanwhile deployed in real-world settings. Most of these applications and the corresponding sensor networks they use are designed as vertically integrated systems [1–3]. In such vertical systems, a sensor network or a limited set of mostly homogeneous sensor networks are deployed for a specific application in mind. The application is mostly the sole user of this sensor network and has a priori knowledge of the capabilities that the sensor network(s) provides. An application also typically knows how to address the respective gateways/sinks of the sensor networks, in order to interact with the sensor networks and shares a common interaction protocol with them.

A method of automatic assessment of feature compatibility in mobile networks

Deployment and upgrade of a mobile network have always been challenging tasks. Very often they require human intervention because telecom networks are complex systems composed of different nodes that need to be compatible in order to communicate and provide network services. Therefore in current telecommunication systems a network expert must check all the requirements and compatibilities of the network prior to activation of a new service. Automation of the assessment of network compatibility is one of the key enablers for Autonomic Management of telecom networks. In this paper we describe a new method for automatic end-to-end assessment of compatibility between network features in a telecom network. The method enables fast, easy and accurate decision making regarding the planning of new feature deployment or the upgrade of already existing features. We built a prototype that demonstrates the described method. It shows that our method is not bound to any type of telecom network and could be used to automate deployment or upgrade of a multiple-domain network.