# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 2 Related Work
A mixed transmission strategy is propose in [22] for energy balancing. The scheme considers not only the distance and energy of nodes but also considers the link reliability and number of neighbor nodes in selecting the relay nodes. The scheme performs better in terms of maximizing the network lifetime in expense of delay. [PERSON] et al. [23] propose a position based routing algorithm for lifetime maximization. The algorithm uses the Forwarding Search Space (FSS) to control retransmission. The function for the selection of forwarder node is based upon its degree, distance and angle. Each time, different set of sensor nodes are selected from this function for data transmission for energy balancing. Due to greater number of calculations involve in selection of data forwarder it delays the performance of the network. Data aggregation is one of the useful concept in WSNs. Many researchers try to minimize unnecessary transmission in order to minimize energy consumption. For this reason an \"Energy Efficient Ant Colony algorithm\" (EEAC) is proposed by [PERSON] et al. [24] such that to gather the data from sensor nodes. In the propose scheme sensor nodes use the remaining energy for finding the next hop data forwarder. The scheme helps to minimize energy consumption and increases the network lifetime in expense of robustness and scalability issue. [PERSON] [25] present a transmission strategy called \"An Optimal-Distance based Transmission Strategy for lifetime maximization of wireless sensor networks\" (ODTS) using ant colony optimization technique. In ODTS, every ant moves from one corona to other corona in order to transmit data to the sink. For lifetime maximization an optimal path solution is developed for energy minimization through Most Energy Efficient Distance (MEED) and energy balancing through Most Energy Balanced Distance (MEBD). The propose scheme prolongs the network lifetime and minimizes the energy consumption in sparse area network however in dense area network its performance is effected due to high traffic load on ants which increases the energy consumption of the network and tends to decrease network lifetime. Energy consumption is one of the major factor which decreases the performance of the network. An energy efficient routing protocol is must in this case. For energy efficiency [PERSON] et al. [26] introduce a routing mechanism that selects minimum hop counts for the selection of forwarder nodes. It selects nodes that have minimum distance from the sink. Moreover it also identifies the link quality while selecting minimum hop counts. Network lifetime is improved in dense area network however its performance is degraded in sparse area network due to the unavailability of forwarder nodes. In order to maximize network performance [PERSON] et al. [23] propose an \"Energy Efficient tree based Data Collection Protocol\" (EEDCP-TB) for data gathering using cascading time mechanism by efficiently allocating time slots in order to save nodal energy. EEDCP-TB helps in maximizing network lifetime in expense of delay. \"Lifetime Maximizing Dynamic Energy efficient routing protocol\" (LMDE) is presented by [PERSON] et al. [27] to optimize network performance. The routing mechanism uses the remaining energy of nodes for data forwarding. The scheme improves the network lifetime however fails to control the scalability and data redundancy. In existing state of the art [12; 25; 28; 29], the authors have adjusted transmission ranges for balancing energy depletion across the network nodes. However, we have adopted the divide and conquer strategy for estimation data load to adjust the transmission power accordingly. To avoid the congestion over the intermediate nodes, an optimal bandwidth is allocated to super nodes according to the data load for improving throughput. Moreover, we have incorporated the computation of data at each hop for avoiding overload of traffic at intermediate nodes. The description of our proposed algorithm is presented in Section 3. Table 1 shows routing techniques comparison.