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Data Gathering And Dissemination In Wireless

A cordless sensor network is special kind of random networks that involves lots of low-cost, low-power, and multi-functional wireless sensor nodes, with sensing, wireless marketing communications and computation features [1, 2, 3]. These sensor nodes converse over a short range with a cordless medium and collaborate to accomplish a common task, like environmental monitoring, military services surveillance, and commercial process control [3]. Wireless sensor systems have open up for new opportunities to observe and connect to the physical environment all around us. They allow us now to accumulate and gather data that was difficult or impossible before [4]. Although Cordless Sensor Networks have given new ways to provide information from variety of applications, irrespective of the type of physical environment, it is seen as a challenging task to extract data from sensor network. Data dissemination and gathering are two conditions used in sensor networks to spell it out two categories of data controlling methods. Data dissemination is a process by which data and concerns for data are routed in the sensor sites while data gathering is to transmit data that has been gathered by the sensor nodes to the base stations. Data gathering protocols try to minimize the power consumption and delay of data gathering process [5]. Although there are distinctions between these two but almost all the literature called collectively as routing protocols. Unlike traditional cellular communications networks such as mobile ad hoc and mobile systems, cellular sensor systems have the following unique characteristics and constraints [3]: high density sensor node deployment, battery pack or no power sensor nodes, low memory space and cpu capacity, self-configurable, unreliable sensor nodes, data redundancy, program specific and powerful topology. Because of above characteristics and constraints of cellular sensor sites, the extraction of data from the network is usually an issue. Therefore, it is important that the look of protocols for data gathering and dissemination needs value these challenges. The primary design challenges of routing protocols for cordless sensor network are: Energy, Finalizing power and Storage area. A number of the design challenges as shown in [3, 6] are highlighted below:

Large volume of sensor nodes: Since most of the wireless sensor networks made up of large sensor nodes, it's very difficult to have an addressing scheme like other cellular networks. The original IP program is not feasible to apply for wireless sensor networks. Moreover, the sensor nodes are deployed at random in hostile environment.

Limited energy capacity: The sensor nodes are battery pack powered, so they have limited energy. That is the main challenges in designing cordless sensor networks. Used, sensor network deployment makes sense only when they can run unattended for a few months and years without working lacking energy [4].

Flow of Data: Almost all the applications of sensor network require the sensory data from multiple options to move towards a single destination node called kitchen sink in contrast to the traditional systems.

Sensor node locations: Most of the proposed routing protocols assumed that the sensor nodes include global setting system (GPS), however in practice it's very difficult to manage the locations of sensor nodes. It is becoming more difficult as sensor networks topology changes frequently due to node failures, moving from the coverage area.

Data redundancy: Data collected by various sensor nodes are usually predicated on common phenomenon; hence the likelihood of data redundancy is high. The routing standard protocol needs to integrate data aggregation techniques to decrease the variety of transmission.

Application Specific: The sensor systems are request specific. The requirement of routing process changes as per the specific software. It is very challenging to create routing protocols which can meet the requirements of all applications.

Scalability: The size of the network develops, so the routing protocols need to be scalable to aid the addition of sensor nodes. All detectors may not always have same functions of energy, handling, sensing and communications. These should be taken care while developing the routing protocols.

Addition to the aforementioned parameters the designing of routing protocols for cellular sensor networks also need to look into following tips [6]: Node deployment

Related work:

Since cellular sensor sites gain its utilization in various request areas, there is a growing interest in this field leading towards continual emergence of new architectural techniques. Wireless sensor network is broadly considered as one of the main solutions of the 21st century [8]. Within this section we draw out and identify how our study differs from the similar research done previously in this area. We also point out the scope and concentrate on group who will benefit from our work.

In [2], similar study was carried out on routing protocols for cellular networks. The information in [2] was released some five years back and many new protocols never have covered. In [3], though it has covered virtually all the routing protocols for cordless sensor systems but it does not provide insight understanding of the protocols. The review is wonderful for readers interested in broad area. The goal of [8] is to provide a comprehensive review on routing techniques focusing on ability to move issues in sensor networks and does not cover all the routing protocols in cellular sensor networks. In such a survey, we draw out the comparative study among wireless sensor network routing protocols bringing their differences and similarities. We also draw out the advantages and drawbacks of different protocols to utilize in several applications of cellular sensor networks. This review would be great for both introductory viewers as well for aspirant researchers who would like to get the complete idea about the current-state-of-art regarding the techniques of data gathering and dissemination in cellular sensor sites. However, we follow [3] in classifying the routing protocols into different categories although we put some additional protocols which are not covered by [3]. We also excluded multipath-based standard protocol category since it falls under data-centric category. Table 1 shows the different categories of wireless sensor network routing protocols influenced by [3]. The representative protocols with (*) grades are our improvements.

Table 1: Routing Protocols for WSNs

Category of Protocols

Representative Protocol

Location-based Protocols

MECN, SMECN, GAF, Items, Period, TBF, BVGF, GeRaF

Data-centric Protocols

SPIN, Directed Diffusion, Rumor Routing, COUGAR, EAD, ACQUIRE, Information-Directed Routing, Gradient-based Routing, Energy-aware routing, Information Directed Routing, Quorum-based Information dissemination, Home Agent-based Information Dissemination, *Flooding, *Gossiping.

Hierarchical-based Protocols

LEACH, PEGASIS, HEED, Teenager, APTEEN

Mobility-based Protocols

SEAD, TTDD, Joint flexibility and routing, Data MULES, Dynamic Proxy Tree-based Data Dissemination, *MDC

Heterogeneity-based Protocols

IDSQ, CADR, CHR

QoS-based Protocols

SAR, Velocity, Energy-Aware Routing.

Data-Centric Protocols

The protocols are differentiated into two categories called data-centric and address-centric. The address-centric routing protocols find the shortest way between source and the destination with addressing program like IP whereas in data-centric routing protocols emphasis is made to search routes from multiple source nodes to a single destination node. Inside the sensor networks, data-centric routing is recommended where data loan consolidation and aggregation is done by the intermediate nodes on the info via multiple sources before sending to the sink node. In this manner, it will save you some energy stopping redundant data transmissions. In this particular section, we spotlight a few of the examples of data-centric routing protocols proposed for cordless sensor networks.

Flooding:

Flooding [5] is a data dissemination method where each sensor node that obtains a packet broadcasts it to its neighboring nodes let's assume that node itself is not the destination of the packet. This technique continues before packet occurs the vacation spot or the maximum hop counts for your packet is reached. Flooding though is a simple and easy to use, but it includes problem like implosion (duplicate concept delivered to the same node) and overlap (duplicate meaning receive by the same node) [2]. Physique 1 and 2 reproduced from [2] shows the implosion and overlap problems in flooding.

Gossiping:

Gossiping [5] is based on flooding, but nodes that will get the packet forwards it only to a single arbitrarily determined neighbor. It avoids implosion issue of flooding and it does not waste as much network resources as flooding. However, gossiping is not really a reliable data dissemination method because the neighbor node is determined randomly, some nodes might not exactly receive that message at all. Moreover, it presents a delay in propagation of data through the nodes [2] since all the nodes which forwards or delivers data need to choose a node.

SPIN:

Sensor Protocols for Information via Negotiation (SPIN) [9, 10] goals to enhance the implosion and overlapping problems of typical flooding process. The SPIN protocols are based on two key mechanisms namely negotiation and resource adaptation [3]. It uses three types of messages [5]: ADV, REQ, and DATA. The sensor node which includes collected data delivers an ADV subject matter using high-level descriptors or meta-data about the actual data. The particular data is transmitted only once the REQ note is received from the interested nodes. This negotiation system avoids the overlapping and implosion problems of classic flooding because the REQ subject matter is sent from the interested node only when it does not have that data. Fig. 3, redrawn from [5] shows how these three messages are exchanged and fig. 4 inspired by [9] and reproduced from [11] shows more detail process who SPIN works.

There are about four variations of SPIN protocols [6, 9, 10]. They are SPIN-PP, SPIN-BC, SPIN-EC and SPIN-RL. Both SIPN-PP and SPIN-BC works under ideal condition when energy is not constraint and packet should never be lost. SPIN-PP tackles the info dissemination problem by using indicate point media while SPIN-BC uses broadcast advertising. There other two protocols will be the modified types of SPIN-PP and SPIN-BC to be able to network which are not ideal. SPIN-EC is really SPIN-PP with additional energy conservation ability. Under SPIN-EC, the nodes participate in data dissemination only when it computes so it has enough energy. In the event the node has abundant energy, it works as same as SPIN-PP with 3-stage handshake. SPIN-RL is a version of SPIN-BC which attempts to recuperate from the deficits in the network by selectively retransmitting the messages.

In SPIN topological changes are localized as each node needs to have information of the next immediate one-hop neighbor only. But this type of protocol can't be found in applications where stability is of increased matter like forest fire and intrusion diagnosis since it generally does not guarantee the info delivery [2]. When the nodes that are interested in data are located very far way and the intermediate nodes aren't interested then your ADV message won't received which will not able to get data.

Directed Diffusion:

Directed Diffusion [12] consists of elements like pursuits, data, communications, gradients and reinforcements. The main goal of the protocol is to use naming scheme to reduce the energy use by avoiding unneeded routing procedures. Interest is a query or interrogation on what individual wants and it contains descriptions of any sensing task. Data is the collected or prepared information of the physical happening which is named using attribute-value pair. Gradient is a link a neighbor from which interest was received, which is characterized by data rate, duration, and expiration time which includes produced from the received interest registered [2]. A node, usually kitchen sink will be broadcasting interest to question data by diffusing interest through its friends and neighbors. The interests are routinely refreshed by the kitchen sink. When this interest is received by the intermediate nodes, they cache for future use, or do in-network data aggregation or immediate interest based on previous cached data. The foundation node sends the data back again through the reverse path of the eye. When data is received by the nodes, they make an effort to compare with the interest cache before. The info which fits the interest is drawn and then delivered via the same way where in fact the interest has received. Out of several paths between kitchen sink and the foundation, one way is picked by network by reinforcement. Once this course is preferred, the sink directs the initial interest again with smaller time period so as to make the foundation node on the determined way to send data more often.

Although directed diffusion has advantages that the protocol can in-network data aggregation and caching which helps you to save energy but this protocol cannot not be applicable to all the applications of wireless sensor systems. The protocol can only be employed to such application which is query motivated. It is not ideal for the applications such as forest fire diagnosis or intrusion diagnosis. Fig. 4, copied from [12] shows the working of the protocol.

Rumor Routing

Rumor routing [13] another variant of Directed Diffusion aims to guide the query to the nodes that have observed event rather than flooding the whole network [2]. It really is a logical compromise between query flooding and event flooding [3]. This protocol is merely useful if the number of queries compared to number of incidents is between the two interaction tips. See fig. 5, redrawn from [13].

Rumor routing algorithms introduces an agent, an extended live packet. A realtor, which also contains an event stand like nodes, trips the network propagating information about local event to the distant nodes. The agent informs the nodes it encounters of any incidents it has observed on its way and at the same time it will synchronize its event desk with the event table of encountered node. An agent will travel the network for several volume of hops and then expire. All of the nodes including an agent maintains an event table list that has event-distance pairs, as shown in fig. 6, copied from [13]. So when a node generates a query for a meeting, the nodes that knows the road, can react to the query by referring its event stand [2]. In this manner, flooding the whole network is averted. Directional rumor routing is proposed in [14], which make an effort to improve latency and energy use by considering query and event propagation in direct line rather than arbitrary walk in normal rumor routing.

Cougar

Cougar [15, 16] is a data source approach for tasking sensor sites through declarative queries. Since in-network computation is much cheaper than transmitting and communication between nodes, cougar procedure proposes a loosely-coupled sent out architecture to support both aggregation and in-network computation. This helps in reducing energy consumption in that way increasing lifetime. The architecture introduces a query proxy level in each sensor node which interacts both with network layer and program layers. The gateway node (where query optimizer is situated) creates a query handling plan after obtaining questions from the sensor nodes. This query plan specifies both data stream between sensor nodes and in-network computation plan at every individual sensor node. The query plan also contains how to choose a head for the query. The query plan can be viewed at non-leader node with the leader node. Fig. 7 and fig. 8, redrawn from [15], show query plan at non-leader node (source sensor) and innovator node respectively.

Although, cougar provides answer to connect to the sensor nodes unbiased from the network part, however the insertion of proxy part at each sensor node introduce extra overhead for sensor node in conditions of memory and energy ingestion [2]. Additional delay may be incurred with the relay striving to wait for the packets from other nodes for aggregation before mailing to the first choice node.

ACQUIRE

ACQUIRE [16] is a data-centric routing protocol aiming at large distributed directories. It is aimed at complex questions which include several sub-queries that are blended by conjunctions or disjunctions within an arbitrary manner. The protocol sends a dynamic query packet in to the network. This effective query packet is dispatched by the kitchen sink, which takes random path or path predefined or guided. The node which gets this effective query packet uses information stored within those to partially solve the query. In the event the nodes don't have up to date information, they gather the information from other neighboring nodes with the distance of d (look-ahead parameter) hops. Once the dynamic query is settled completely, the response is sent back to the node which has released the query. Some of the assumptions manufactured in this protocol are [17]: the sensors, with same transmission range are organized uniformly in a region and they are stationary and do not fail.

EAD: Energy-Aware Data-Centric Routing

Energy-Aware Data-Centric (EAD) [18] aspires to create a electronic backbone containing all active receptors, which is in charge of in-network data control and relaying traffic. The radios of other nodes that are not in the electronic backbone are put off to conserve the. The sensor network is displayed by way of a broadcast tree rooted at the gateway and spanning all the receptors with large leaf nodes. In order to conserve ability, the radios of the leaf nodes are put off as the nodes that happen to be in virtual backbone are lively for traffic relaying. The protocol attempts to construct broadcast spanning tree network with maximum leaf nodes so that maximum energy can be conserved. The idea of EAD is to include the neighboring broadcast arranging and the sent out competition among neighbours, based on residual energy [18]. The efficiency of the protocol would be more when how big is the network is small. When how big is the network is large, execution time could be more since the execution process propagates from the kitchen sink to the whole network. Other protocol like the main one proposed by Shah and Rabaey in [19] also aims at increasing network life time. They use network survivability as the primary metric and propose to choose one of the multiple pathways with a certain probability so that the whole network life increases. But the protocol assumes that each node is addressable with some addressing schemes.

Information-Directed Routing

Location-based Protocols

Since sensor nodes have limited energy capacity, almost all of the routing protocols try to reduce the usage of energy in routing functions. In the majority of the protocols location of the sensor nodes are used to get the distance between two communicating pairs in order to find the perfect path with low energy usage. If location of a specific sensor node is known, query can be delivered to that one location only without mailing to other areas which will reduce the number of transmitting significantly [2]. Location-based protocol makes use of the position information to relay data to the network rather than the whole network. In this section, we illustrate some of the location-based routing protocols proposed for wireless sensor sites.

Minimum Energy Communication Network (MECN):

Hierarchical-based Protocols

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