A survey and comparison on localisation algorithms for wireless ad hoc networks <s> Radio communication constraint models <s> Position information of individual nodes is useful in implementing functions such as routing and querying in ad-hoc networks. Deriving position information by using the capability of the nodes to measure time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) and signal strength have been used to localize nodes relative to a frame of reference. The nodes in an ad-hoc network can have multiple capabilities and exploiting one or more of the capabilities can improve the quality of positioning. In this paper, we show how AOA capability of the nodes can be used to derive position information. We propose a method for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability. <s> BIB001 </s> A survey and comparison on localisation algorithms for wireless ad hoc networks <s> Radio communication constraint models <s> The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. In this paper we study node localization, a component technology that would enhance the effectiveness and capabilities of this new class of networks. The n-hop multilateration primitive presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed. <s> BIB002 </s> A survey and comparison on localisation algorithms for wireless ad hoc networks <s> Radio communication constraint models <s> A novel task-oriented self-organization algorithm that accounts for mostly location-dependent tasks and heterogeneous sensors inherent in dense ad-hoc sensor systems is proposed. It forms a sensor group for an announced task by sequentially selecting the best matched sensors using a leader election algorithm and a residual task calculation algorithm. To improve the associated communication overhead, the sensor node location information is used in task broadcasting, thus confining the algorithm implementation to a dynamically maintained contributor group which comprises of those sensors which may contribute to the task. Sensor localization is based on a refinement of an algorithm in which utilizes only the neighborhood information of each sensor node corresponding to its each preset radio transmission power level. The proposed self-organization algorithm and how various system parameters affect its performance are examined via extensive simulations. In a densely deployed sensor system, when the refined localization scheme is demonstrated to achieve very good localization, the proposed self-organization algorithm consistently yields a sensor group that covers the announced task. <s> BIB003
Radio communication constraint models are a set of geometry rules that can be used to bound position estimates. Generally, they may consist of a combination of the radial and angular constraints, and n-hop neighborhood constraints BIB003   . Radial constraint means that when a node B can hear a node A, the distance between them is less than A's radio transmission range. When multiple transmission powers are used by an anchor node, other nodes that receive the radio signal from that anchor, may estimate their possible position by signal strength BIB003 . In Figure 1 Angular constraint refers to the fact that when a node gets best reception at a certain angle, then it can estimate the relative angle to the source transmitter, which may be a cone  . A small group of neighbors may form a set of triangles with geometry constraints to further precise their position estimates BIB001 . In Figure 1 (c), node A 3 can be determined with relative angles of ac and ab. N-hop neighborhood constraint is a combination of individual constraints from neighbors. Satisfying this constraint will generally yield smaller feasible region for each node. The n-hop constraint is especially important for min-max boundary based and aggregation based methods like n-hop multilateration algorithm BIB002  and APIT algorithm  . In Figure 1 