Internet protocol over wireless sensor networks, from myth to reality

Internet Protocol (IP) is a standard network layer protocol of the Internet architecture, allowing communication among heterogeneous networks. For a given network to be accessible from the Internet it must have a router that complies with this protocol. Wireless sensor networks have many smart sensing nodes with computational, communication and sensing capabilities. Such smart sensors cooperate to gather relevant data and present it to the user. The connection of sensor networks and the Internet has been realized using gateway or proxy- based approaches. Historically, several routing protocols were specifically created, discarding IP. However, recent research, prototypes and even implementation tools show that it is possible to combine the advantages of IP access with sensor networks challenges, with a major contribution from the 6LoWPAN Working Group. This paper presents the advantages and challenges of IP on sensor networks, surveys the state-of-art with some implementation examples, and points further research topics in this area

Service-oriented wireless sensor networks and an energy-aware mesh routing algorithm

Service-oriented wireless sensor networks (WSNs) are being paid more and more attention because service computing can hide complexity of WSNs and enables simple and transparent access to individual sensor nodes. Existing WSNs mainly use IEEE 802.15.4 as their communication specification, however, this protocol suite cannot support IP-based routing and service-oriented access because it only specifies a set of physical- and MAC-layer protocols. For inosculating WSNs with IP networks, IEEE proposed a 6LoWPAN (IPv6 over LoW Power wireless Area Networks) as the adaptation layer between IP and MAC layers. However, it is still a challenging task how to discover and manage sensor resources, guarantee the security of WSNs and route messages over resource-restricted sensor nodes. This paper is set to address such three key issues. Firstly, we propose a service-oriented WSN architectural model based on 6LoWPAN and design a lightweight service middleware SOWAM (service-oriented WSN architecture middleware), where each sensor node provides a collection of services and is managed by our SOWAM. Secondly, we develop a security mechanism for the authentication and secure connection among users and sensor nodes. Finally, we propose an energyaware mesh routing protocol (EAMR) for message transmission in a WSN with multiple mobile sinks, aiming at prolonging the lifetime of WSNs as long as possible. In our EAMR, sensor nodes with the residual energy lower than a threshold do not forward messages for other nodes until the threshold is leveled down. As a result, the energy consumption is evened over sensor nodes significantly. The experimental results demonstrate the feasibility of our service-oriented approach and lightweight middleware SOWAM, as well as the effectiveness of our routing algorithm EAMR.<br /

INTERNET OF THINGS ARCHITECTURES:MODELING AND IMPLEMENTATION CHALLENGES

Internet of Things (IoT) encompasses a broad set of technologies, hardware and software stacks.The rapid evolution and broadened scope can be attributed to the inclusion of many existing mature technologies like the wireless sensor networks, RFID and a wide variety of custom solutions and newer smart devices.There is a growing need for devices to collaborate to provide the desired service.The heterogeneity coupled with theresource constrained nature of the devices seriously limits the choices in design. The capability to onboard billions of devices on to the existing infrastructure without degrading the quality of service is robust programming frameworks are in place more crucial. Automation enables devices to act independently which can be enabled only by ensuring.Architectural modelsaddressingthe challenges like scalability, distributiveness, interoperability and programmability are the need for the hour

A survey on software-defined wireless sensor networks : challenges and design requirements

Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639hb2017Electrical, Electronic and Computer Engineerin

Participatory Cloud Computing: The Community Cloud Management Protocol

This thesis work takes an investigative approach into developing a middleware solution for managing services in a community cloud computing infrastructure predominantly made of interconnected low power wireless devices. The thesis extends itself slightly outside of this acute framing to ensure heterogeneity is accounted for. The developed framework, in its draft implementation, provides networks with value added functionality in a way which minimally impacts nodes on the network. Two sub-protocols are developed and successfully implemented in order to achieve efficient discovery and allocation of the community cloud resources. First results are promising as the systems developed show both low resource consumption in its application, but also the ability to effectively transfer services through the network while evenly distributing load amongst computing resources on the network

Wireless sensor systems in indoor situation modeling II (WISM II)

fi=vertaisarvioimaton|en=nonPeerReviewed

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa