REVIEW ON HIERARCHICAL ROUTING IN WIRELESS SENSOR NETWORKS

The Wireless Sensor Network(WSN) has become an interesting field of research of the 21st century. It is a type of the wireless ad-hoc network. This has brought about developing low cost, low-power and multi-function sensor nodes. The network life for wireless sensor network plays an important role in survivability. Energy efficiency is one of the critical concerns for wireless sensor networks. Sensor nodes are strictly constrained in terms of storage, board energy and processing capacity. For these reasons, many new protocols have been proposed for the purpose of data routing in sensor networks. These protocols can be classified into three main categories: data-centric, location-based and hierarchical. This paper mainly deals with some of the major Energy-efficient hierarchical routing protocols for wireless sensor networks. First we will discuss the energy-efficient Hierarchical routing protocols in brief along with their important features, objectives, drawbacks and area of application. Finally, we provide a comparison of these various protocols

A Data-Centric Mechanism for Wireless Sensor Networks with Weighted Queries

[[abstract]]Wireless sensor networks (WSNs) are characterized by their low bandwidth, limited energy, and largely distributed deployment. To reduce the flooding overhead raised by transmitting query and data information, several data-centric storage mechanisms are proposed. However, the locations of these data-centric nodes significantly impact the power consumption and efficiency for information queries and storage capabilities, especially in a multi-sink environment. This paper proposes a novel dissemination approach, which is namely the dynamic data-centric routing and storage mechanism (DDCRS), to dynamically determine locations of data-centric nodes according to sink nodes' location and data collecting rate and automatically construct shared paths from data-centric node to multiple sinks. To save the power consumption, the data-centric node is changed when new sink nodes participate when the WSNs or some queries change their frequencies. The simulation results reveal that the proposed protocol outperforms existing protocols in terms of power conservation and power balancing.[[sponsorship]]IEEE Taipei Section; National Science Council; Ministry of Education; Tamkang University; Asia University; Providence University; The University of Aizu; Lanzhou University[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20091203~20091205[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks

Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067

Amorphous Placement and Informed Diffusion for Timely Monitoring by Autonomous, Resource-Constrained, Mobile Sensors

Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance