## 1 Introduction and Motivation  
One of the main design challenges in wireless sensor networks (WSNs) is coping with resource constraints placed on individual sensor devices. One of the resource constraints to meet is power consumption. As physical size of a sensor node decreases, so does energy capacity [1]. Energy constraints end up creating limitations such as computational power and limited coverage that lead to architectural issues. In dense deployments spanning large areas, the sensor network usually has a mesh topological structure [2]. In such deployments, a number of sensor nodes route their messages via other nodes in the network. Some of the sensor nodes are configured to turn their power on and off (various power modes may be available) in order to minimize energy consumption. Therefore, when two neighboring sensors have to communicate, both the nodes must be in active (on) mode [2]. Clustering protocols help in achieving this objective in a scalable and efficient manner by organizing sensor nodes into small groups called cells or clusters. Communication cost is a crucial factor in deciding the network life time reflected by the number of messages passed among the neighboring nodes. In wireless sensor networks, clustering involves grouping sensor nodes and electing a clusterhead (CH) similar to having base transceiver stations (BTS) in cellular communication. Nodes within a cluster/cell can directly communicate with their CH. CHs forward aggregated data to the central coordinating station (CCS) directly or via multiple hops using other CHs as intermediate forwarding nodes. Thus, the collection of clusterheads in the network forms an overlay network of sensor nodes (see Figure 1 for architecture diagram). In single hop clustering algorithms, various studies make assumptions regarding direct communication between CHs to the CCS. Such assumptions may seem too strict for realistic situations as, in some cases, the CH may have limited transmission range. Figure 1: Hierarchical cooperative communication architecture. Network longevity is one of the fundamental design objectives of wireless sensor networks [3]. In recent years, cooperative MIMO has been proposed as a communication model to be used in ad hoc wireless sensor networks [3; 4; 5]. Multi-input multi-output (MIMO) technology has the potential to increase channel capacity and reduce transmission energy consumption. Because of the requirement of energy efficiency in large networks, a concept known as virtual MIMO has attracted a growing interest. In virtual MIMO network, a group of sensor nodes cooperate to transmit and receive data. This technique is also known as cooperative MIMO (CMIMO). Participation of multiple transmitters and receivers in a transmission saves significant energy in long-range communications [6]. Energy can be conserved if we allow nodes to cooperatively transmit data just like in MIMO mode of communication. Due to circuitry complexity and difficulty of integrating separate antennas on sensor nodes, cooperative sensor nodes exploit virtual MIMO in wireless sensor networks for energy efficient communication and enhance data reliability [3]. Hierarchical routing (HR) is an efficient routing approach in wireless sensor networks. In hierarchical routing, for a given network setting, nodes are grouped into small clusters at base level 0, which are further grouped in to bigger clusters at level 1 and so on. This provides excellent scalability by providing a node state of O(logN) for routing and addressing [7]. In [8], [PERSON] _et al_. discuss capacity scaling laws of wireless ad hoc networks using hierarchical cooperation. Their findings show an order optimal linear throughput scaling is achievable for some networks using hierarchical cooperation (HC). Wireless sensor networks pose stringent constraints in terms of energy efficiency, network lifetime and data reliability.