Secondly in communication systems, many random variations may occur at top application layer and bottom physical layers. As WSNs are supposed to have extremely lengthy network lifetime and make efficient utilization of their limited perished power sources generally battery , much of the recent research is on adopting cross-layer approach. A brief overview of recent work on cross-layer optimization for minimizing energy consumption is given below:. Though XLM performs receiver based contention but every node here is to decide about the participation in the communication which itself increases the broadcasting burden of RTS and CTS packets.
As a result of which energy cannot be saved to a desirable length. In the network set-up phase, a power detector is used which helps in awakening the nodes whenever the transmission of a neighbor is scheduled. This reduces idle listening period but raises complexity of circuitry design of sensor nodes. Coarse-grained and Fine-grained scheduling has been implemented on the working nodes of each sub grid and all other nodes are turned to sleep mode.
Though the schemes are good enough for static and scenario aware networks but what if the network changes dynamically. It is difficult to stay awake the other nodes, if there is a message to transmit or receive. The average packet service time along with a weighted cost function is used to choose a most appropriate routing path for sensor nodes to transmit.
This further helps to reduce end-to-end delay and thereby enhances energy efficiency. But to maintain strict end to end delay deadlines is not viable in all the cases. The authors used values of k, m Nakagami parameter and received gamma value to achieve targeted packet error rate PER. The Markov chain model along with Nakagami parameter is further used to adjust channel fading and estimating channel state transition probabilities. The packet length and average round trip time RTT at data link layer together decides the delay constraints whereas after retransmissions, the probability of packet loss was calculated which then considered as the PER upper bound.
To optimize the system performance in terms of transmitted bits per symbol, received signal to noise ratio SNR and PER upper bound has been considered.
Wireless ad hoc network
Authors further suggested that for a known number of bits to be transmitted, an optimal constellation size can be calculated which further helps to reduce energy consumption by reducing total transmission. It is clearly shown that rated adaptation can lead to significant energy saving of the network. It is shown in that by implementing new coding schemes, decoder energy can also be reduced significantly. Authors further claimed power saving of the network by reducing idle listening and decreasing number of collisions during transmissions.
Cross-layer integration of MAC and routing layers conserve significant amount of energy of WSNs as is suggested in   , but the work provides only analytical results without any communication design. The issues of latency and multi hop performance during cross layer information exchange are also discussed. In this protocol authors dynamically adjusted the transmission power of nodes and the selection of next hop node is based on its residual energy and link quality.
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The varying channel link capacity due to interference put an impact on topology change and if the channel is multicast, due to fading it becomes even more challenging to use exiting protocols. To overcome such problems authors suggested cross-layer optimization between transport, MAC and physical layers. Authors used single-hop cluster based topology to simulate the throughput performance, though it can also be used on different topologies. The entire sensing area is divided into ordinary sensor nodes, cluster heads and border nodes.
The ordinary sensor nodes monitor the environment and transmit its data to their respective cluster head nodes. The cluster head nodes in turn gather data from all ordinary sensor nodes of the cluster compress and aggregate the received data and then forward it to the border nodes. These border nodes are selected in such a way that these are supposed to be closer to Base station.
The main focus of this algorithm is to reduce hop distance among sensors and to reduce energy consumption during data transmission and reception. During the set-up phase, the base station collects the information of the position of all sensor nodes within the sensing area along with their unique identification number. To form uniform clusters and choosing cluster heads and border nodes, following steps are to be followed:.
Figure 1 depicts cluster architecture of the sensing area. The Base station broadcasts the routing information and time schedule in the form of fixed or variable TDMA slots to all the members of the clusters i. Hence each sensor node has its own routing table and TDMA schedule, to follow. Figure 1. Cluster architecture. The proposed UCLEAH algorithm provides a centralized network topology and as the circuitry design of sensor nodes is simple with limited battery and processing capabilities, the channelization can be done by the base station only.
The The payload data contains actual data to be transmitted. It also contains different types of frames like management, data and control frames. Each frame further consists of MAC header, payload information and frame check sequence. Optimizing data packet length in wireless networks to minimize energy consumption has found considerable attention in literature  . In  estimated bit error rate is used to optimize the packet size whereas in  error correction bits added on packet size improves energy efficiency.
Instead of preamble, a pattern sequence 11 or 00 for channel estimation can be added for better synchronization between transmitting sensor node and cluster head. Figure 2 shows the proposed packet structure. Figure 2. Packet structure. Time taken to transmit a data packet of r bits is given by:. The above equations clearly depicts that the total energy consumption during transmission and reception of a data packet is directly proportional to the number of bits contained in data packet and the hop distance.
Thereby the proposed packet structure and UCLEAH algorithm both helps to achieve lesser total energy consumption of the network. Secondly capacity enhancement through frequency re-use of the sensing network can be done using the following equation:. The base station or sink allots time slots and assigns frequency channels using above equations to all the sensors of the network.
Using energy consumption and symbol error formulas given by   in the Table 1 below, two scenarios have been drawn:. In this scenario, when BPSK, 8-PSK modulation techniques being very simple to generate and demodulate are applied on all the members of the cluster, these performed good in terms of energy efficiency but proved fairly in-effective in terms of channel throughput and data rates whereas when 16PSK and 4QAM modulation techniques being complex to design and demodulate are applied on all cluster members, these techniques provided high channel throughput but consumed good chunk of energy of the network.
In this scenario, it is observed that when the information transmitted with in a distance of up to 35 - 40 meters all the modulation schemes performed almost in a similar manner whereas when the distance increases say above 40 meters BPSK, 8PSK performed poorly in terms of channel throughput on the other hand 16PSK or 4QAM modulation schemes provided high channel throughput but are little expensive in terms of consumption of energy. Table 1.
Table of energy consumption and symbol error formulas. The focus of this paper is to extend the network lifetime to maximum and also to balance the entire network in terms of channel throughput and data rates. It is therefore, recommended that sensor nodes within the diameter of 35 to 40 meters be run on BPSK or 8PSK modulation schemes and when the distance between sensor nodes and their respective cluster head and cluster head and border node is above 40 meters 16PSK or 4QAM modulation techniques should be implemented.
With this way we cannot only successfully sense the area for much greater time but also with effective data rates and channel efficiencies. Figure 3 and Figure 4 shows formation of clusters with cluster heads and border nodes of each cluster. Figure 3. Figure 4. Default parameters used during simulation are given as Table 2 under:. Figure 5 shows the energy consumption Vs Noise of different modulation schemes at a fixed distance of 30 m.
It is observed that for any modulation scheme, the probability of receiving a data packet decreases with the increase in packet size. Therefore, small packet sizes may help to increase energy efficiency. However it is true only if no packet overhead is considered. Figure 6 shows the trade-off between the energy consumption and varied packet sizes with different packet overheads. The trade off in packet size with or without overhead can be seen in the above figure. When packet overhead is zero or minimum, energy tends to decrease but with the rise in packet overheads, the packet size also increases.
Table 2. Parameters used in the models. Figure 5. Energy consumption Vs Noise at a fixed distance of 30 meters. Figure 6.
Energy consumption during varied packet sizes different packet overheads. Equations 1 to 4 clearly shows that distance and transmission time are directly proportional to the energy consumed. Figure 7 gives us the fair idea about energy consumption at varied transmission time for short distance. On the other hand complex circuitry design of 16PSK or 4QAM techniques needs more processing capabilities of sensor nodes which in many cases may not be possible and feasible too.
Due to this trade off, it is highly recommended through this paper that to balance the entire network, a heterogeneous modulation technique may be implemented on the sensing area i. This way not only network lifetime can be extended but higher data rates can also be achieved. A multi-tier decision mechanism is designed to detect running devices.
Sensor nodes try to identify the running devices based on audio data and only the result of identification is reported to the sink. If a sensor node fails to identify running devices, audio data is reported to the sink where a sophisticated identification is performed based on time and energy consumption. Cooperative communication has attracted great attention of research, as it can be employed to achieve spatial diversity without requiring multiple transceiver antennas on the same device.
However, the common theme of most research in this field is to optimize physical layer performance, e. In this project, we study the cross layer design for video streaming over cooperative wireless multimedia sensor networks. The objective is to maximize the sum peak signal-to-noise ratio PSNR of multiple video sessions, by jointly regulating the video encoding rate at application layer, deciding the optimal relay selection strategy and controlling the transmission power for each source and relay node at lower layers.
The research concentrates on at least three aspects: 1 Derive the upper bound for sum PSNR and study the interactions among video rate control, power allocation and relay selection. Extension of the research includes incorporating the mobility of nodes and routing, employing the game theory to study the fairness among multiple nodes. Related Publications S.
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